On the nature of phonological cues in the acquisition of French gender categories: Evidence from instance-based learning models

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Lingua 120 (2010) 879–900 www.elsevier.com/locate/lingua

On the nature of phonological cues in the acquisition of French gender categories: Evidence from instance-based learning models Clive Matthews * School of Language and Communication Studies, University of East Anglia, Norwich NR4 7TJ, United Kingdom Received 20 May 2008; received in revised form 15 June 2009; accepted 26 June 2009 Available online 23 July 2009

Abstract A crucial element in acquiring a language involves partitioning its words into different grammatical categories. Although fundamentally the determination of grammatical gender in a language such as French is based on recognising certain distributional patterns, a noun’s phonological ending has also been implicated as providing an important additional cue as to an unknown noun’s gender value. Not only does the literature which explores this possibility rarely make precise the notion of ‘‘final sounds’’ but also those that are referred to appear not to be consonant with either phonological or psycholinguistic theory. This paper explores the suitability of the final syllable as a reliable cue. The issue is investigated through an instance-based machine learning model, in part because it more closely fits with psycholinguistic assumptions of gender processing. The results of the simulations show that not only does the final syllable prove a reliable indicator but that it is, in fact, more reliable than most other sequences. The paper, however, remains agnostic as to the exact role of analogising in either gender acquisition or gender processing. Tentative suggestions are made that gender attribution may require a dual mechanism account. # 2009 Elsevier B.V. All rights reserved. Keywords: Gender assignment; Category learning; Instance-based models; TiMBL; French gender

1. Introduction Some part of lexical acquisition involves mapping individual words onto an appropriate set of grammatical categories. This entails identifying a set of intrinsic properties in virtue of which such category membership is determined. Various suggestions have been proposed as to what types of property these might be in general, including semantic (Bowerman, 1973; Macnamara, 1982), distributional (Harris, 1951; Fries, 1952), and phonological (Kelly, 1992; Morgan and Demuth, 1996). In recent years research has tended to focus mainly on the latter two suggestions. Evidence has been found with respect to distributional cues, for example, to show that not only does the input language provide reliable information for categorisation in a variety of cases (Brent, 1994; Cartwright and Brent, 1997; Mintz et al., 2002), but that learners, both adult and child, are sensitive to these indicators (Brooks et al., 1993; Go´mez and Gerken, 2000; Mintz, 2002, 2003; Gerken et al., 2005). Similar findings have also been presented with respect to a variety of phonological cues such as syllable length, stress position, and so on (Cassidy and Kelly, 1991; Kelly, 1992, * Tel.: +44 1603 593430. E-mail address: [email protected]. 0024-3841/$ – see front matter # 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.lingua.2009.06.007

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1996; Morgan et al., 1996; Shi et al., 1998, 1999). More recent research has shown that a combination of such cues may result in more effective forms of categorisation (Shi et al., 1998; Durieux and Gillis, 2001; Monaghan et al., 2005). The focus of the current paper is on the nature of the phonological cues which may be used to determine a French noun’s grammatical gender. In particular, it explores the question of whether the final syllable of a noun is a reliable cue as to its gender, a suggestion first made by Hardison (1996) but one that has never been explicitly followed up. Although the developmental literature has examined the usefulness of various types of cue in categorisation, there is surprisingly little discussion on the nature of the mental machinery assumed to underlie this process. This lacuna has been partially filled by research using machine learning techniques (e.g. Cartwright and Brent, 1997; Redington and Chater, 1998; Shi et al., 1998; Durieux and Gillis, 2001). Even though this work does not necessarily commit itself to the view that human learning involves precisely the same processes, it is argued that the results are of some significance because they offer ‘‘principled conceptions of learning . . . [which provide an] . . . empirical measure of the utility of potential sources of information . . . [and can also] . . . suggest new avenues for experimental work’’ (Redington and Chater, 1998:130). Since the cues studied, especially the phonological, tend to be probabilistic and, hence, capable of violation, it is not surprising that connectionist models (Bechtel and Abrahamsen, 2002) have often been to the fore in such machine learning work since they implement general statistical principles and allow for ‘‘soft constraint’’ satisfaction (Redington and Chater, 1998). An alternative machine learning approach, which equally allows for ‘‘fuzzy’’ category boundaries, uses instance-based models of classification (e.g. Daelemans and van den Bosch, 2005; Durieux et al., 1999; Durieux and Gillis, 2001; Skousen et al., 2002). It is these latter models that will provide the empirical support to the current investigation. The reasons for choosing such models are outlined below. 2. Gender assignment Much of the grammatical research cited above has focussed on determining which distributional and/or phonological cues may be utilised to best differentiate between either open- and closed-class categories in general or, within the open class, between nouns and verbs (typically in English). Another category which has received quite detailed consideration is that of grammatical gender. Gender is a category which partitions the nouns of a language into different classes in virtue of certain agreement relations (see Corbett, 1991, for extensive discussion). In French, the focus of attention of this paper, for example, each noun is associated with one of two gender values on the basis of agreement patterns with, amongst others, different forms of the definite and indefinite articles as well as with certain adjectives: le/un gros chien (‘‘the/a big dog’’) vs. la/une grosse vache (‘‘the/a big cow’’). On the basis of this distribution chien is assigned to the gender category ‘‘masculine’’ and vache to the category ‘‘feminine’’. Gender, unlike noun and verb, is not a universal category being estimated to occur in about 75% of the world’s languages (Mallinson and Blake, 1981). The fact that the category has provoked so much interest is possibly accounted for by the apparently arbitrary nature of the gender values associated with particular nouns. In French, for example, whereas some names for seats are masculine – e.g. sie`ge (‘‘seat’’), fauteuil (‘‘armchair’’) – others are feminine – e.g. chaise (‘‘chair’’), banquette (‘‘bench/window seat’’). On the basis of examples of this sort Bloomfield (1933:280) famously wrote that ‘‘there seems to be no practical criterion by which the gender of a noun in German, French or Latin [can] be determined’’. The complexity of this assignment task is supposedly demonstrated by the difficulties that second language learners experience with it; indeed, Tucker et al. (1977:11) claim that for such learners it is the ‘‘single most frustrating and difficult part of the study of French’’ and experimental data certainly shows that it is highly problematic (Tucker et al., 1977; Surridge and Lessard, 1984). Somewhat confusingly, however, it does not seem to pose the same problems for first language learners; Clark (1985:706), for example, claims that by the age of 3 years French children make almost no errors in attributing nouns to the correct gender class. Similar facts seem to apply to other gendered languages (e.g. see, Rodina, 2007 for examples from Russian). Bloomfield’s claim, of course, cannot be that there are no cues for gender determination. Clearly, distributional information is available; this, after all, is how the categories are defined in the first place. Observing that sie`ge and fauteuil occur in the frame le/un ___ whilst chaise and banquette occur in the context la/une ___ is enough to establish that the first two nouns are masculine and the second pair feminine. The possibility that speakers are aware of such distribution frames is suggested by the finding that they are significantly slower to identify the gender of a noun when using the category labels masculine and feminine compared with the articles le or la (Desrochers and Paivio, 1990; Desrochers et al., 1989). Bloomfield’s point, then, must be understood to mean that there is no apparent inherent

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lexical property which allows a learner on first encountering, say, fauteuil out of any syntactic context to decide that it is masculine rather than feminine. If the learner has to wait until hearing a noun in the context le/un ___ or la/une ___ to be able to correctly assign it to a gender category, it would seem that each noun has to be classified and stored on a word-by-word basis. Some have found this conclusion problematic. Tucker et al. (1977:14), for instance, argue that it would entail ‘‘a vast and never ending task which would be neither economical nor practical’’. This claim, however, is not very persuasive. After all, lexical storage requires large amounts of memory to record each word’s idiosyncratic features: its phonological form, syntactic properties (including lexical category, derivational properties, subcategorisation requirements, thematic relations, etc.) and semantics. This necessary requirement of memorisation equally constitutes ‘‘a vast and never ending task’’ from Tucker et al.’s perspective, but is clearly one to which the human mind is equal. Factoring in additional gender information does not, therefore, appear to hugely increase the informational load to which most theories are already committed (Matthews, 2005:113–114). There are, however, various reasons for suspecting that there may be more to gender assignment than a pure reliance on distributional information. Not least amongst these reasons is the fact that native speakers are able to consistently assign gender values to previously unknown, including nonce, nouns without any reference to a syntactic context (Tucker et al., 1977; Holmes and Dejean de la Baˆtrie, 1999). Other evidence comes from research on the acquisition of miniature artificial languages (see Go´mez and Gerken, 2000 for a review). Smith (1969), for example, devised an artificial language consisting of four disjoint categories M, N, P and Q, the ‘‘lexical’’ members of which were letter names, with ‘‘sentences’’ being either of the form MN or PQ. Following training on a subset of possible sentences Smith’s informants were unwilling to accept new sequences which started with either an N or Q category word but were just as likely to accept sequences of the illegal form MQ/PN as the legal MN/PQ. In other words, the subjects appeared to have generalised position information but failed to recognise the co-occurrence – in effect, gender-like – dependencies between these two positions. Subsequent research has shown, however, that learning such restrictions is only achievable when a subset of members of the categories exhibits some form of partial similarity. Braine (1987), for instance, illustrated this by providing semantic cues, in particular natural gender indicators, to half of the words in the N and Q categories. Brooks et al. (1993) and Frigo and McDonald (1998) report similar findings when words share common phonological beginnings or endings. Although these results do not necessarily indicate that gender categories in real languages cannot be learned purely on the basis of distributional evidence, they do suggest that semantic and phonological cues may facilitate the process. Another potential difficulty for a learner relying purely on distributional information is that not all nouns are clearly distinguished in the types of syntactic context previously mentioned. In particular nouns beginning with a vowel are preceded by an elided definite article which does not distinguish for gender: l’escargot (‘‘the snail’’, masculine) vs. l’escorte (‘‘the escort’’, feminine).1 Given the high frequency of the definite article–noun combination, it may well be the case that for such nouns a learner fails to encounter a disambiguating context, especially for low frequency words. Interestingly, speakers are both slower and more prone to error on gender decision tasks involving vowel initial nouns (Desrochers and Brabant, 1995; Desrochers and Paivio, 1990; Desrochers et al., 1989; Taft and Meunier, 1998). In addition to these particular issues, more general problems with purely distributional mechanisms have been suggested in the literature. Pinker (1984:49–50), for example, argues that the number of distributional relationships that are potentially relevant in any categorisation task would overwhelm any unconstrained learning mechanism. He further maintains that in a number of cases distributional analysis would lead to spurious generalisations, that the most linguistically informative properties are typically abstract and so not directly observable, and that those properties that are observable are not applicable in all languages.2 Although these last objections are, perhaps, not totally wellfounded (see Redington and Chater, 1998, for discussion), the conclusion drawn on the basis of each of the points in the last few paragraphs is that, although of central importance in determining French gender categories, distributional information may well be usefully complemented by other cues. 1

A similar situation arises with the possessive pronouns which also usually vary with respect to gender – mon chien vs. ma vache (‘‘my dog/ cow’’). Again, when the noun begins with a vowel the same invariant (masculine) form is used: mon escargot vs. mon escorte. 2 An element of circularity has also been noted with respect to distributional analyses. For example, a noun might be identified as the kind of word which can occur following a determiner. However, the category of determiner may equally be characterised as the kind of word which can occur immediately preceding a noun. With (French) gender categories, however, this particular problem does not arise in the core grammatical contexts since they are specified with respect to specific lexical items, i.e. le/la, un/une, mon/ma, ce/cette, etc.

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3. Non-distributional cues to French gender assignment Corbett (1991:8) claims that for all gendered languages ‘‘there is a semantic core to the assignment system’’. In its purest form (found, for example, in a number of Dravidian languages, including Tamil) the gender of any noun can be precisely predicted in virtue of its semantics (Corbett, 1991:8–9). Although Corbett does not spell out the precise nature of this core, some have argued for the primacy of the notion of animacy (Dahl, 2000). The effect of semantic cues on gender acquisition (and especially their interaction with other cues) has been explored in a wide range of work (e.g. Zubin and Ko¨pcke, 1986; Josefsson, 2006; Nesset, 2006; Rice, 2006; Schwichtenberg and Schiller, 2004; Enger, 2009; Thornton, 2009). Recall also that Braine (1987) has shown their utility within the artificial language learning paradigm. Pinker (1984), in the light of his criticism of purely distributional mechanisms mentioned in the previous section, adopts a semantic bootstrapping theory of (general) grammatical category acquisition which hypothesises that learners are able to make use of various semantico-cognitive categories as an initial means of mapping onto a set of grammatical categories (argued to be innately presented to the child). Once a suitably large number of words are so categorised, the learner is assumed to detect that these items also share certain distributional properties, and it is these that are eventually used to determine later classifications. Pinker’s particular bootstrapping story for gender assignment is that the child initially uses ‘‘the sex of human referents as a semantic cue for the feature name GENDER and the feature values masc and fem, with the gender of inanimate nouns learned distributionally via their similarity in inflection to words denoting humans’’ (Pinker, 1984:172; see also Mulford, 1985). Indeed, there is a precise correlation between the semantic categories of [MALE] and [FEMALE] and the grammatical categories of masculine and feminine: if [MALE] is part of the semantic definition of a noun, it is, of necessity, masculine. Similarly with [FEMALE] and feminine. So, cerf, being defined as ‘‘an adult, male deer’’ is masculine. On the other hand, although it is a contingent fact that sentries are typically male [MALE] is not part of the semantic definition of sentille and so it would not be contradictory for the word to be feminine (as, indeed, it is). Aside from natural gender other semantic feature/gender–value correlations have been noted in French. Surridge (1989a), for example, points out that the days of the week, months of the year, seasons and the names of wines and cheeses are, in the vast majority of cases, masculine, whilst Rice (2006) observes that names denoting roads or paths are typically feminine. Although these other semantic correlations have been noted in the linguistic literature, natural gender has remained the focus of attention in psycholinguistic studies. This is not surprising since not only is this semantic category known to be well-established from a relatively young age (Fagot and Leinbach, 1993; Gelman et al., 1986), but there is also experimental evidence to suggest that the classification of inanimate objects into biological gender classes by speakers (of gendered languages) is sensitive to the corresponding noun’s grammatical gender (Sera et al., 1994, 2002; Boroditsky et al., 2003). There are problems, however, in assuming that semantic categories play the kind of role that Pinker seems to assume (at least, in French). Not least amongst these is the fact that the natural/grammatical gender correlation only applies to a very small number of lexical items. For example, the list presented in Arrive´ et al. (1986:288) only gives 23 pairs of words such as garc¸on/fille (‘‘girl/boy’’), cerf/biche (‘‘stag/hind’’), and e´talon/jument (‘‘stallion/mare’’). Further a number of these words occur with such low frequencies that they are unlikely to be known to the young learner.3 This then appears to be a very limited dataset on which to base the type of process envisaged by Pinker. A more telling observation, however, is that a variety of studies have revealed that young children learning gender categories are surprisingly immune to semantic information with regards to gender attribution (Karmiloff-Smith, 1979; Levy, 1983; MacWhinney, 1978; Mills, 1986; Pe´rez-Pereira, 1991). Karmiloff-Smith, for example, showed that when French speaking children are presented with a drawing of an imaginary object which is clearly female but whose (nonce) name has phonological properties typically associated with masculine nouns, they will overwhelmingly

3 For example, of the 46 words in the list, nine (=20%) (consœur (‘‘female colleague’’), bru (‘‘daughter-in-law’’), belier (‘‘ram’’), jars (‘‘gander’’), hase (‘‘doe’’), laie (‘‘wild sow’’), guenon (‘‘male monkey’’), verrat (‘‘boar’’), truie (‘‘sow’’)) have a frequency of less than two occurrences per million words in the Lexique 2 database of 31 million words (New et al., 2004). That said, a number of the remaining words (including fre`re (‘‘brother’’), sœur (‘‘sister’’), garcon (‘‘boy’’), fille (‘‘girl’’), homme (‘‘man’’), femme (‘‘woman’’), pe`re (‘‘father’’), mere (‘‘mother’’)) are amongst the most frequent in the language.

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classify it as masculine rather than feminine. This suggests that the children are more attuned to phonological rather than semantic properties as potential cues to gender values. The fact that there are significant correlations between a French noun’s phonological ending and its gender value is well documented (Bidot, 1925; Mel’cuk, 1974; Tucker et al., 1977). For example on the basis of their dataset, Tucker et al. (1977) note that 94.2% of (the 1453) nouns ending in / / are masculine, whilst 90% (of the 612) ending in /z/ are feminine. Not that all endings are as unequivocal: 51.4% of (the 214) nouns ending in /p/ are feminine whilst 50.1% (of the 2791) ending in /e/ are masculine. In some cases, such non-predictive results may be improved by extending backwards from the final phoneme: for example, 92.7% of words ending in /te/ are feminine, whilst 98% of nouns ending in /je/ are masculine.4 A number of studies have shown that native French speakers produce responses on a variety of gender decision tasks which indicate that they are sensitive to these ‘‘sublexical’’ phonological regularities (e.g. Desrochers and Paivio, 1990; Desrochers et al., 1989; Holmes and Dejean de la Baˆtrie, 1999; Holmes and Segui, 2004; Karmiloff-Smith, 1979; Stevens, 1984; Taft and Meunier, 1998; Tucker et al., 1977). Similar findings have been reported for German (MacWhinney, 1978; Mills, 1986), Hebrew (Levy, 1983) and Italian (Bates et al., 1995). It would appear that it is these properties that enable speakers to assign, and agree upon, gender values to nonce items (Tucker et al., 1977; Holmes and Dejean de la Baˆtrie, 1999). One of the weaknesses of the literature cited above lies in the imprecision of its terminology. So, although discussion is replete with locutions such as ‘‘ending’’, ‘‘ending patterns’’, ‘‘cluster of final sounds’’, ‘‘terminaisons phone`tiques’’ (for a range of examples see Ferrand, 2001:11; Holmes and Dejean de la Baˆtrie, 1999:481; Holmes and Segui, 2004:427, 429, 430; Kelly, 1992:355; Koehn, 1994:36; Mu¨ller, 1990:203; Surridge and Lessard, 1984:44), in the vast majority of cases nothing is said as to exactly what unit of phonology is being referred to.5 In Tucker et al.’s (1977) exhaustive work, for example, correlations of varying strengths are presented for endings ranging from 1 to 5 phonemes. There is no indication, though, as to how a learning mechanism is supposed to discover this plethora of regularities, nor how to choose amongst the competing choices on any particular occasion of assignment. Further, as Carroll (1989) was perhaps the first to point out, many of these proposed phonemic sequences have no status in phonological (and/or lexical) theory and so are unlikely units over which a speaker may be assumed to compute generalisations. Partly as a response to this type of criticism Hardison (1996) suggested that the correlations might best be stated over the noun’s final syllable. She argues that this idea is supported by a variety of psycholinguistic findings. For example, there is evidence for the syllable’s crucial role in the perceptual segmentation of French words (Cutler et al., 1986; Cutler and Norris, 1988; Mehler et al., 1981; Dumay et al., 2002) which suggests that ‘‘the syllable in French may serve as an efficient prelexical access code with stored representation in syllable form in the lexicon’’ (Hardison, 1996:24). In addition, the relative perceptual prominence of the final syllable has been noted (Delattre, 1938) and Slobin (1985) has argued for the usefulness of salient markers in category acquisition. Further, we might add to Hardison’s supporting evidence that, at least for Dutch, it has been shown that word naming latencies are subject to final syllable frequency effects (Levelt and Wheeldon, 1995). What is more, a variety of studies have indicated that young infants’ representations of utterances are in terms of syllables (Bertoncini and Mehler, 1981; Jusczyk and Derrah, 1987). Given these findings, Hardison’s idea that gender values might also be associated with this structural property does not appear unreasonable. Unfortunately, she fails to provide any quantitative details which support this claim. As noted in the introduction the main experimental aim of this paper is to explore her suggestion within a machine learning environment. As an aside, another aspect of syllabicity may also offer a potential cue to gender values. Kelly (1992) has shown that the number of syllables can be a useful cue in distinguishing between the grammatical categories of noun and verb in English: nouns tend to have more syllables than verbs. Further, evidence suggests that speakers, including infants, are sensitive to this property (Kelly, 1992:352–53; Bijeljac-Babic et al., 1993). A similar relationship between syllable number and gender values has also been noted with respect to German. Arndt (1970), for example, shows that there is a 4 It is not possible to collapse individual rules into more general statements along the lines of, say, ‘‘Nouns ending in high vowels are feminine’’: so, for example, although nouns ending in /i/ are typically feminine, those ending in the other French high vowels /u/ and /y/ are characteristically masculine. 5 Indeed, this is a weakness in the treatment of other languages. Corbett (1991) is equally imprecise in his discussion of a variety of languages although in most cases the relevant phonological unit is clear.

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Fig. 1. Ratio of masculine to feminine nouns as a function of syllable count.

strong tendency for monosyllabic nouns to be non-feminine, whilst polysyllabic nouns are more likely to be nonneuter. Ko¨pcke (1982:45) reports similar findings: amongst the monosyllabic nouns 64% are masculine (compared with a figure of 50% for the lexicon as a whole), 14% feminine (30%) and 22% neuter (20%). What does not seem to have been previously noted in the literature is that a similar correlation also occurs in French. Put in simple terms masculine nouns tend to have fewer syllables than feminine nouns. More precisely, on the basis of a set of 29,940 nouns extracted from the Lexique 2 database (New et al., 2004) the average number of syllables for masculine nouns is 2.575 whilst that for feminine nouns is 3.007. The Mann–Whitney U-test shows that the difference between these two means is significant (z = 26.978, p > .001). Further, as Fig. 1 shows, for up to 3 syllables masculine nouns predominate (60.14% of 23,330 nouns, compared with 54.71% masculine nouns for the dataset as a whole), whereas for 4 syllables and above feminine nouns not only predominate (64.45% of 6610 nouns) but increasingly so. The relevance of these observations in the current context is limited, however, given the unlikelihood of many 5 or more syllable words forming the input to child learners. Indeed, with respect to words in the datasets used in the simulations reported on below none was more than 4 syllables long (and the 4 syllable items only constituted 0.4% of the total, 6/1565) and the vast majority (87.16%) consisted of either 1 or 2 syllables. 4. Computational models of gender assignment There is, then, a significant range of evidence, both statistical and psycholinguistic, to suggest that some aspects of its phonology may be a useful adjunct to distributional data when cueing a French noun’s gender assignment. Partially on the basis of such observations, although typically only implicitly so, a small number of computational gender assignment simulations have been explored (some with languages other than French). The models investigated have either been connectionist (e.g. MacWhinney et al., 1989 (German); Sokolik and Smith, 1992 (French); Matthews, 1999a (French); Rodrigues and Boivin, 2000 (French); Smith et al., 2003 (Spanish)) or instance-based (e.g. Durieux et al., 1999 (Dutch); Eddington, 2002a,b (Spanish); Matthews, 2005 (French); Marchal et al., 2007 (French)). The results from each of these studies show varying degrees of classificatory accuracy. For example, with reference only to the connectionist studies on French, Sokolik and Smith’s (1992) network correctly classified around 85% of the nouns in their training set,6 Rodrigues and Boivin (2000) report their network’s performance at a little under 100%, whilst those in Matthews (1999a, 2005) are at 100%. With respect to the test datasets Sokolik and Smith’s networks performed at an average of just over 76% accuracy, although Rodrigues and Boivin and Matthews’ results were considerably lower at 54% and 53%, respectively. Rather than using connectionist models, this paper adopts an alternative instance- or exemplar-based approach to machine learning. The basic idea of such models is that a set of individual examples along with their categorisation are 6

This relatively poor result is mainly a function of the small number of training cycles used and, more especially, the lack of a hidden layer of units in the network.

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stored in memory and that classification of unknown items proceeds by identifying those instances to which this example is most similar and then projecting (analogising) their classification onto the target. The psychological literature on categorisation has reported a number of exemplar effects (see Smith and Medin, 1981; Shanks, 1995 for summaries) and a variety of mathematical models have been developed to account for them (e.g. Medin and Schaffer, 1978; Nosofsky, 1988) along with associated computational simulations (Aha et al., 1991; Kolodner, 1993). Indeed, rather than being of peripheral interest, a recent review of the literature has claimed that in fact ‘‘exemplar models have the edge in the current battle of category-learning experiments’’ (Murphy, 2002:114). Compared with interest in connectionist approaches, instance-based theories – either psychological or computational – have received relatively little attention in the mainstream linguistic literature apart from the field of Natural Language Processing (NLP) (see Daelemans and van den Bosch, 2005; Skousen et al., 2002 for reviews) where accounts can be found of such phenomena as co-reference and anaphora resolution (Cardie, 1996), the assignment of word stress (Daelemans et al., 1994), word sense disambiguation (Fujii et al., 1998) and machine translation (Jones, 1996). This lack of linguistic interest is somewhat surprising given that each of the various properties which have recommended connectionist theories to some researchers – such as the possibility of dispensing with rule-systems, fuzzy categorisation, etc. – receive equally plausible explanations within instance-based approaches (Murphy, 2002). Further, the little computational work which has compared connectionist and instancebased performance on particular tasks has found, at least, their ‘‘weak’’ classificatory behaviour to be highly similar (Daelemans et al., 1993; Matthews, 2005). With respect to gender attribution instance-based systems have a number of potential advantages over more traditional accounts. For example, the statistical nature of the regularities finds a natural form of expression in the construction of the set of similar examples from which a value will be chosen. This property, of course, equally applies to connectionist accounts. One advantage of instance-based accounts over the latter, however, is that they more closely accord with the standard assumption in the psycholinguistic literature that ‘‘gender is not computed, but rather stored . . . as part of each noun’s grammatical description in the mental lexicon’’ (Schriefers and Jescheniak, 1999:577). Connectionist models, on the other hand, assume gender is a mapping between phonological forms and gender values. Partial support for the storage assumption is provided by reports of French aphasic patients who are able to state the gender of a word even though they are unable to produce – i.e. access the phonological representation of – the word itself (Henaff Gonon et al., 1989). Similar ‘‘tip-of-the-tongue’’ phenomena have been reported for normal speakers (Ferrand, 2001). van Turennout et al. (1998) study using LRP brain potentials also shows that (Dutch) speakers have access to a noun’s gender value before the word’s phonological form becomes available.. A further advantage of instance-based over connectionist models is that it is permissible to associate different classification values to the same input representation. There are two broad classes of case where this is required with respect to gender: ‘‘les noms epicenes’’ where the noun’s gender depends upon the natural gender of the referent, what Dahl (2000:106) calls ‘‘referential gender’’ – e.g. un/une enfant (‘‘child’’) – and various homophonous nouns with different semantics – un moule (‘‘mould’’) vs. une moule (‘‘mussel’’). To enable a connectionist network to associate the same phonological forms with different output classifications a disambiguating feature needs adding to the input representation, somewhat weakening the claim that gender assignment is being determined on the grounds of phonological form. Instance-based accounts are not hampered in this way since there is nothing inconsistent with the same form being associated with different categories. For reasons such as these instance-based models appear to offer a suitable machine learning environment in which to address the question of whether a noun’s final syllable offers a suitable cue for French gender assignment. 5. A Computational simulation of gender attribution via phonological cues This section provides a brief description of the particular instance-based model used in the simulations, TiMBL, along with the datasets and an outline of the principle results. 5.1. The computational model Within the computational literature a distinction is drawn between ‘‘eager’’ and ‘‘lazy’’ machine learning algorithms (Aha, 1997). Lazy learning involves no pre-processing of the training data, involving little more than the

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storage of instances and their classification. Eager learning, on the other hand, involves the construction of abstract, summary representations of the data, perhaps in the form of rules or prototypes,7 which permit individual instances to be discarded. The lazy, instance-based simulations reported here used the Tilburg Memory-Based Learner (TiMBL) software package developed by the Induction of Linguistic Knowledge (ILK) research group based at Tilburg University and the University of Antwerp.8 A brief review of the details of TiMBL’s algorithm follows (for a more detailed description see Daelemans et al., 2004; Daelemans and van den Bosch, 2005). As in any instance-based model, all previously encountered items along with their classification (the ‘‘training instances’’) are stored in memory as feature vectors. The classification process involves presenting the system with a test (usually new) example. This instance, X, is compared for similarity with each example, Y, in memory using a distance metric, D(X, Y), in order to identify the most similar instances (the ‘‘nearest neighbours’’) to the target. The basic ‘‘overlap’’ metric used in TiMBL is defined by the equation: DðX; YÞ ¼

n X

dðxi ; yi Þ

(1)

i¼1

where xi are yi are the ith features of X and Y, respectively, and: dðxi ; yi Þ ¼ 0 if xi ¼ yi and 1

if xi 6¼ yi

(2)

for xi and yi symbolic features. In other words, the distance between X and Y is the sum of the differences between the values of each vector feature. For example: Dð < s; e; l; l; e > ; < s; a; l; l; e > Þ ¼ 1 Dð < s; e; l; l; e > ; < c; h; a; m; p > Þ ¼ 5

(3)

These values simply reflect the fact that salle is more similar to selle than is champ. Although by default TiMBL identifies only the closest nearest neighbours in memory, it is possible to extend the set to more distant neighbours (k-NN). Whatever the choice of value for k, a set of nearest neighbours is determined. Typically the classification associated with the majority of items in this set is assigned to the test example although an alternative means of extrapolation would be to choose a value at random from this set. This latter form of selection would permit leakage across the category boundary allowing for ‘‘fuzzy’’ classification. The distance metric as defined in (1) assumes that each feature in the vector is of equal importance. However, it may well be that some feature positions are better category predictors than others. To handle this possibility a weighting function, w, expressing each feature position’s predictive value can be added to the definition of D: DðX; YÞ ¼

n X

wi dðxi ; yi Þ

(4)

i¼1

The calculation of this information-theoretic measure involves comparing the difference in uncertainty for each feature position in predicting the classification of an instance depending on whether the value for that feature is known or not. The effect of w in (4) is that those instances which match on important features are categorised as nearer neighbours in comparison with those only matching on less important attributes. The values of the weighting function for each feature position in a particular dataset is calculated prior to the classificatory phase of the process. Since the application of feature weighting typically leads to improved performance, their use is of some import in the NLP literature and TiMBL makes available a number of different functions for experimentation. Their use, however, will be of less concern in this paper. In part this is because the psychological validity of such weighting functions is unknown but also because their use introduces an element of abstraction which compromises the ‘‘indolence’’ of a pure instance-based approach which is the theoretical focus of this paper. The main use of weighting functions in subsequent discussion will be as an analytic tool to identify those features within a representation which provide the most relevant information in predicting the input’s gender value. 7 Because the mapping of a connectionist network represents the ‘‘statistical central tendency’’ of the input data, it has been argued that networks represent the computational equivalent of prototype categories (McClelland and Rumelhart, 1986:182ff; Churchland, 1989:209–218). 8 TiMBL can be downloaded from http://ilk.uvt.nl/mblp.

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5.2. Datasets The classificatory behaviour of TiMBL depends upon the makeup of the training database both in terms of which instances are included and how they are represented. In the experiments reported here the database consists of the 1565 nouns contained in the children’s dictionary Le Gros Dico des Tout Petits (Duhamel and Balaz, 1993). In comparison with previous connectionist research this constitutes a relatively large training set9 but is much smaller than typical TiMBL simulations.10 It is worth bearing in mind, however, that this latter work has typically been conducted with an eye to issues relating to NLP rather than the kind of developmental concerns which underlie the current work. Since, as argued above, it is plausible to assume that access to phonological cues is a beneficial supplement to distributional data during the early stages of gender learning, the interest here is in the type of restricted database likely to be known to a child rather than the French lexicon as a whole. Although it has been claimed that restricted lexical subsets present an ‘‘invaluable start in the acquisition of gender’’ (Surridge, 1993:87), this only applies if the smaller database exhibits similar correlations as those exhibited by the language as a whole. This is generally the case for the database under consideration (see Surridge, 1989b for similar comments regarding the distributions found in the Dictionnaire Fondamental de la Langue Franc¸aise (Gougenheim, 1958)).11 Similarly, the set’s 3:2 masculine:feminine split (939:626) reasonably closely matches that exhibited by a more complete lexicon.12 The relationship between the number of syllables and gender values previously mentioned is also highly similar although of little predictive value in this context since the vast majority (=99.6%, 1559/1565) of the items are of 3 syllables or less and, hence, typically masculine. The data was coded up in a variety of representational formats. The basis of these was the phonemic representation of each word as recorded in Le Petit Robert (Rey and Rey-Dobove, 1988) and transcribed into an ASCII code. In order to explore the cue potential of word endings the set was coded in both a right- and left-justified form using 8 feature positions; empty positions were filled with the null variable, =. If a word’s ending is more predictive of its gender value, the expectation would be that the right-justified encoding would result in better classification overall. Each word was also coded syllabically with each syllable consisting of six feature–values representing an onset (of up to three consonants), nucleus and coda (of up to two consonants).13 Two different encodings were used, both centred on the vowel, in fourth position, but differing in their consonantal alignment. The first represented the onset and coda immediately before and after the vowel (with any unassigned positions filled with the null variable): accordingly, in this format the syllable /tik/ would be represented by the vector =,=,t, i, k, =. With the second coding the onset and coda were justified to the left and right, respectively: t, =,=, i, =, k. The dataset was encoded four times (in both formats) from 1 to 4 syllables (justified to the right) with any empty syllable position represented as a string of six empty variables. Notwithstanding the previous observation that gender categories in French are not susceptible to abstract phonological characterisation (fn. 4), a number of the training sets were also transcribed into a simple set of distinctive 9

Rodrigues and Boivin (2000), for example, use a training set of 900 nouns whilst that employed by Sokolik and Smith (1992) consists of only 450 examples. 10 For example, the work on German plural formation described in Daelemans and van den Bosch (2005, §3.1) uses a training set of 25,168 instances. Note, however, that Eddington’s (2002b) work on Spanish gender, also within an instance-based framework, uses a database of 2416 nouns. 11 The main discrepancies lie with words ending in (1) / / of which 86.2% (of 94) are masculine compared with 29.8% (of 2665) in Tucker et al. (1977) and (2) /i/ of which 63.0% (of 92) were masculine compared with 24.6% (2336) in Tucker et al. There are a few other differences but based on very small numbers of instance. Since there is some evidence from the artificial language learning literature (e.g. Braine et al., 1990) that learners are only sensitive to items above a certain degree of frequency, it could be argued that such small subsets should be discounted irrespective of whether they are discrepant or not. 12 The precise masculine/feminine distribution for the language as a whole is unclear. For example, 54.71% of the 29,940 nouns found in Lexique 2 (New et al., 2004) are masculine whilst the comparable figure for Tucker et al.’s (1977) 32,022 word set is 61.70%. 13 The structure of the French syllable is controversial. Government Phonology (Kaye, 1990), for example, assumes that word-final consonants are actually onsets to empty headed syllables—so that patte (‘‘animal’s leg’’) would have the disyllabic structure pa.tØ (where Ø is an empty nucleus) rather than the monosyllabic structure assumed in this paper. Although it is not immediately clear that such assumptions would lead to significantly different results in the present case, it might be worth pursuing in future work. Since, however, instance-based learning is closely related to multipletrace theory in phonology (Pisoni, 1997), which takes the detailed phonetic realisations as the basis of phonological storage, it seems appropriate to assume an input–output faithfulness of the kind proposed in Optimality Theory (Kager, 1999). Another representational issue worth following up is the idea that lexical phonological entries may be highly underspecified as implied by the assumptions of Declarative Phonology (Scobbie, 1997; on French coda stops, see Lodge, 2005).

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Table 1 Examples of different representational formats of boutique. Orthographic Phonemic (right justification) Phonemic (left justification) Syllable Encoding 1 (1 syllable) Syllable Encoding 1 (2 syllables) Syllable Encoding 1 (3 syllables) Syllable Encoding 2 (2 syllables) Distinctive features (last two phonemes)

b, o, u, t, i, q, u, e, F =, =, =, b, u, t, i, k, F b, u, t, i, k, =, =, =, F =, =, t, i, k, =, F =, =, b, u, =, =, =, =, t, i, k, =, F =, =, =,=,=,=,=,=, b, u, =, =, =, =, t, i, k, =, F b, =, =, u, =, =, t, =, =, i, =, k, F vow, clo, unr, fro, con, v-l, sto,vel, F

features. In particular, each phoneme was coded in terms of four features with the first distinguishing vowels from consonants. Consonants were further characterised in terms of voice, manner and place of articulation (based on Tranel, 1987:23–27) – /k/, for example, being coded as con(sonant), v(oice)-l(ess), sto(p), vel(ar) – and the vowels via features of aperture, lip rounding and place of articulation (Tranel, 1987:27–30) – /i/ being represented as vow(el), clo(sed), unr(ounded), fro(nt). Finally, the database was also represented in standard orthographic form. This set was included as a point of comparison only since any results are of limited import when considering the behaviour of preliterate children. Some example representations for the feminine noun boutique (‘‘shop’’, /bu.tik/) are illustrated in Table 1. It could be argued that since stress assignment in French is not lexical, it might be reasonable to assume that instances of prosodic words should also be represented. The implication would be that these would provide 100% reliable indicators of the gender class of the head noun. This, in effect, is a variation on the idea that each noun is associated in the lexicon with an article of the correct gender—e.g. lelivre, uneporte, etc. (Gre´goire, 1947; Sourdot, 1977; see also MacWhinney, 1978, for a similar proposal with respect to German). Some possible psycholinguistic support for this possibility comes from Desrochers et al. (1989) who found that informants respond quicker with labels such as un vs. une when categorising nouns compared with masculin vs. fe´minin. Note, however, that irrespective of whether prosodic words are stored by speakers or not, this is irrelevant to the issues of concern here since the assumption is that such distributional information is unavailable given that the nouns are presumed to be unknown. Unlike connectionist simulations TiMBL does not require the test set to be distinct from the training set since each instance of the latter may be treated as a test case (with the remainder of the set acting as the training set). It is the results of this ‘‘leave-one-out’’ testing that are reported below. 5.3. Results Table 2 presents TiMBL’s accuracy scores for the dataset’s basic forms of encoding. Given that extending the search to k = 3 nearest neighbours does not improve performance, only the results of the k = 1 simulations will be referred to in subsequent discussion. Similarly, since the form of syllabic representation does not significantly alter the outcome (McNemar tests showing the differences to be statistically insignificant) only results for the first type of syllabic encoding will be reported in the remainder of the paper. The assumption that the noun’s ending provides the best locus of information for gender assignment is confirmed by the higher accuracy figures for the right- compared with the left-justified phonemic coding. In fact, the best results (not shown) are achieved with only the final 2 or 3 right-justified phonemes when the accuracy figures rise to 75.53% and 75.91%, respectively (x2 = 7.313, p > .007). The general impression is that as the total amount of cue-information decreases, so the accuracy of the classification improves. An examination of the Gain Ratio (GR) weightings corroborates the importance of the word’s ending. Recall that weighting is an information-theoretic measure of the significance of each feature position in predicting the relevant category labels. Amongst the various weighting options available in TiMBL, GR was consistently found to produce the best results. Fig. 2 shows that the highest values for GR calculated by TiMBL over the dataset are associated with the final three feature positions of the input. Feature 1 is high since 83% of the nouns having a feature in this position are masculine. This feature position, however, only takes a value in just over 1% of the dataset and, hence, in general is of little predictive value. As previously noted, TiMBL’s results using feature weighting will not be discussed in detail in this paper. For the record, however, we report that the use of GR in general does not greatly increase the accuracy of the classification; for

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Table 2 TiMBL’s classificatory accuracy with respect to Le Gros Dico des Tout Petits database (with no weighting function applied to the similarity metric). Dataset encoding

% correct k-NN = 1

k-NN = 3

Phonemic: right justified Phonemic: left justified

72.78 67.41

68.43 61.98

Syllable Encoding 1 4 syllables Final 3 syllables Final 2 syllables Final syllable Rhyme (of final syllable)

72.59 72.65 72.59 75.14 74.57

70.86 70.93 69.39 63.83 59.74

Syllable Encoding 2 4 syllables Final 3 syllables Final 2 syllables Final syllable Rhyme (of final syllable)

72.27 72.20 72.20 74.95 74.44

70.67 70.73 69.39 62.94 59.74

Fig. 2. Gain Ratio feature weights computed over the complete database represented phonemically with right justification.

example, the dataset encoded just using the rhyme of the final syllable rises from 74.57% to 74.95%. The exception to this is the (right-justified) phonemic encoding which improves from 72.78% to 74.06%. In general moving to k-NN = 3 also improves performance with the best figure, 77.12%, recorded for the phonemic representation of the dataset; perhaps surprisingly, however, the figure for Final Syllable drops to 63.83%. A similar pattern of results emerges with the syllabic representations: the highest accuracy figure (75.14%) is achieved on the basis of the final syllable only. Further, the distribution of GR weightings indicates that it is the feature positions associated with the rhyme which provide the main indices for classification as shown in Fig. 3.14 Due to the skew of the database towards masculine nouns, the accuracy results reported in Table 2 do not necessarily present a precise performance measure of the system’s classificatory behaviour. For instance, the simple strategy of classifying every noun as masculine would result in 60% accuracy overall with all of the masculine nouns correctly categorised; of course, as a consequence, 100% of the feminine examples would be incorrectly classified. In fact, exactly this pattern of results occurs in a number of simulations where k-NN is extended beyond 1. This is a 14

Although there is only 0.57 difference in accuracy between the two classifiers, Final syllable and Rhyme, McNemar’s statistic shows this difference to be significant (x2 = 34.671, p > .001).

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Fig. 3. Gain Ratio feature weights for the final syllable (composed of an onset of 3 consonants, vowel and a coda of 2 consonants) computed over the complete database.

consequence of the analogical set of neighbours becoming so large for each test example that masculine nouns always predominate in the set. Within the machine learning literature a variety of classification measures based on the confusion matrix computed from the test results shown in Fig. 4 have been developed to give a better picture of the performance of a system. The metrics reported here are defined as follows. Accuracy is the proportion of instances correctly classified as either being members of that class or not. TP þ TN Accuracy ¼ (5) TP þ FP þ FN þ TN Precision is the proportion of times that instances are correctly classified for that particular category, i.e. the probability of an instance being of that class given that it is classified as such. TP Precision ¼ (6) TP þ FP Recall is the probability of an instance being classified as a member of its correct class. TP Recall ¼ (7) TP þ FN The False Positive Rate (FPR) is the probability of an instance from another class being incorrectly categorised as a member of the class in question. FP False Positive Rate ¼ (8) FP þ TN Finally, the F-score is the harmonic mean of precision and recall. 2  precision  recall F-score ¼ (9) precision þ recall Tables 3 and 4 report these performance measures across the various encodings for the respective categories of masculine and feminine.

Fig. 4. Confusion matrix for a class-specific categorisation (where TP = true positives, FP = false positives, FN = false negatives and TN = true negatives).

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Table 3 Evaluation metrics for the class of masculine nouns. TP

FP

TN

FN

Accuracy

Precision

Recall

FPR

F-score

Phonemic: right justified Phonemic: left justified

719 702

206 273

420 353

220 237

0.728 0.674

0.777 0.720

0.766 0.748

0.329 0.436

0.771 0.734

Syllable Encoding 1 4 syllables Final 3 syllables Final 2 syllables Final syllable Rhyme

750 750 760 791 836

240 239 250 241 295

386 387 376 385 331

189 189 179 148 103

0.726 0.727 0.726 0.751 0.746

0.758 0.758 0.752 0.766 0.739

0.799 0.799 0.809 0.842 0.890

0.383 0.382 0.399 0.385 0.471

0.778 0.778 0.780 0.805 0.808

Table 4 Evaluation metrics for the class of feminine nouns. TP

FP

TN

FN

Accuracy

Precision

Recall

FPR

F-score

Phonemic: right justified Phonemic: left justified

420 353

220 237

719 702

206 273

0.728 0.674

0.656 0.598

0.671 0.564

0.234 0.252

0.664 0.581

Syllable Encoding 1 4 syllables Final 3 syllables Final 2 syllables Final syllable Rhyme

386 387 376 385 331

189 189 179 148 103

750 750 760 791 836

240 239 250 241 295

0.727 0.727 0.726 0.751 0.746

0.671 0.672 0.677 0.722 0.763

0.617 0.618 0.601 0.615 0.529

0.201 0.201 0.191 0.158 0.110

0.643 0.644 0.637 0.664 0.624

The F-scores show that across all simulations masculine nouns are more accurately predicted (average 0.779) than feminine (0.637). Note an interesting difference between these means: with the masculine nouns, the recall probability is (one simulation aside) higher than precision, whilst with the feminine instances the opposite holds. In other words, TiMBL is relatively more conservative in predicting feminine compared with masculine nouns but those that are identified as being feminine have a higher probability of being correctly classified. For both classes it is clear from the F-scores that the left-justified phonemic encoding is the least successful and that Final Syllable is at least the equal to, if not better than, the other versions. As expected, the simulations using the distinctive feature encodings failed to produce any significant improvements over the phonemic representations. For example, the overall accuracy rate for the right-justified dataset was 70.22% (compared with 72.78% for the phonemic equivalent, x2 = 2.016, p > 0.156) with F-scores of 0.746 (0.771) for the masculine nouns and 0.695 (0.664) for the feminine instances. The Final Syllable coding, on the other hand, showed a small improvement of 75.34% (in comparison to 75.14%, x2 = 1.556, p > 0.001) which was the result of a slightly better performance on the feminine nouns (F-score 0.684 compared with 0.664); the masculine results were essentially the same: 0.798 vs. 0.805. By far the best results were provided by the orthographic simulations where 81.15% of the dataset was correctly classified when justified to the right; even the left-justified format produced an accuracy score of 76.10%. The GR feature weighting reported in Fig. 5 again shows the importance of the final feature positions. Indeed, on the basis of only the last three graphemes TiMBL’s accuracy increased to 84.15%. Such results are perhaps not surprising since often orthography helps to distinguish nouns with the same phonemic ending but different gender values; for example, on the basis of the data provided by Tucker et al. (1977:84–85), 73.21% of nouns ending in /n/ and spelt are feminine, but this figure changes to 95.2% masculine for those nouns without the final .15 The results from Tucker et al.’s (1977) informants when the test examples are presented in oral-graphic mode indicate that native speakers are sensitive to such cues. A similar sensitivity to this property by 15 Although ‘‘Nouns ending in are feminine’’ is often cited as an aide-memoire, this is only of limited utility for language learners being neither a necessary condition – not all feminine nouns end in – nor sufficient – for example, 31% of the 1030 nouns ending in in Surridge’s (1995) word list of high frequency nouns are in fact masculine.

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Fig. 5. Gain Ratio feature weights computed over the complete database represented orthographically with right justification.

TiMBL is indicated by a recall figure of 0.896 for feminine nouns on the basis of the final two graphemes; the corresponding figure for the masculine nouns is 0.802. That said, the issue of orthographic representations will be put aside here given the paper’s concern with the potential utility of non-distributional gender cues for pre-literate language learners but may be of some interest for adult second language learners. 6. Discussion In some respects the results from the simulations reported in the preceding section are of limited in their consequences. Recall, the basic goal was simply to investigate, within a machine learning environment, Hardison’s (1996) claim that a French noun’s syllabic form – and, more particularly, its final syllable – provides as reliable a cue as to its grammatical gender as other phonological sequences; a conjecture which TiMBL’s results support. This finding, however, is intuitively unsurprising given Carroll’s (1989) argument that ceteris paribus it would be expected that the unit of computation ought to be one consonant with phonological and psycholinguistic theory. And yet as compelling as this observation is most work subsequent to Carroll’s article has remained predicated on vague (and variable) reference to ad hoc notions of ‘‘clusters of final sounds’’. Even in recent sophisticated work such as Holmes and Segui (2004, 2006), for example, it is clear from their word lists that ‘‘endings’’ for them can be a single phoneme (e.g. /o/ in taureau/agneau), two phonemes (e.g. /es/ in vitesse/addresse) or more (e.g. /ite/ in since´rite´/intensite´); rarely do their proposed endings correspond to the word’s final syllable. Further, as the since´rite´/intensite´ example shows, they also fail to distinguish between derived and non-derived nominal endings (-ite´ being a derivational affix creating nouns from adjectives), a point returned to below. If the work reported here has no further merit, it ought at least to provide support for Hardison’s conjecture that future experimental research would be better justified in calculating any phonological effects over final syllable structure. This aside the paper does not commit itself to a particular version of gender processing in terms either of acquisition, comprehension or production. It does assume that a gender value is a syntactic property stored as part of the noun’s lexical entry and that determining a noun’s gender during processing is, derived nominals perhaps aside, a matter of lexical look-up rather than computation on the basis of the noun’s phonological structure. This is based on the recognition that a noun’s gender is ultimately an arbitrary property. In this sense an instance-based learning model has more in common with Levelt’s influential theory of lexical processing (Levelt, 1989; Levelt et al., 1999), where a noun’s lemma node connects directly to a gender node without direct reference to its phonological form, than say that implied in connectionist approaches (Sokolik and Smith, 1992; Matthews, 1999a; Rodrigues and Boivin, 2000). Adopting an instance-based model does not, however, entail many commitments on how the lexical entries are initially determined; it certainly does not follow, for example, that they be established analogically. Given that phonological correspondences are not absolute, this is desirable. So, it does not matter, for example, that over 99% of

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nouns ending in /a / are masculine, plage (/pla / ‘‘beach’’) still has to be categorised as feminine as is evidenced by its appearing in construction with the feminine determiners une and la.16 This is why gender acquisition has to be fundamentally (and ultimately) based on the recognition of distributional properties. There must, however, be more to the acquisition process than a mere reliance on syntactic cues. Studies using the artificial language learning paradigm, for example, have indicated that accuracy of recall on the equivalent of gender categories, even when the items are presented in syntactic context, is significantly aided when the subclasses share phonological (Brooks et al., 1993; Frigo and McDonald, 1998) or semantic properties (Braine, 1987). In fact, Frigo and McDonald (1998:236) claim that for such a partition to be acquired ‘‘the subclasses must be systematically marked’’ (emphasis added) although the marking need not be complete; only 60% of the ‘‘marked’’ sets of both Brooks et al. (1993) and Frigo and McDonald (1998) had the same endings. It does not follow, however, from the superior performance of their subjects in recalling the marked sets in comparison with unsystematic and unmarked conditions that phonological properties are the sole arbiter of gender categories; indeed, their subjects’ recall was above the level of chance with the datasets with no systematic marking. Further, Mintz’s (2002) work has indicated that categories can be formed solely on the basis of distributional patterns, a position supported he argues by studies which have examined the distributional information available to learners in child directed speech (Mintz et al., 2002; Mintz, 2003). The apparent conclusion is that any theory of grammatical category acquisition must accord a role for both distributional as well as semantic and phonological cues. The exact details of such a model will be complicated not only by having to allow for computation over, and integration of, rather different types of information structure but also because the role of these differing cues are likely to change with the context. Monaghan et al. (2005), for instance, provide evidence, which implies that although distributional cues provide reliable cues for high-frequency words, phonological cues appear more useful in categorising low frequency items. They suggest that this may be on the grounds that if the word is heard in a context only once, it may have occurred in error; repeated occurrences of a word in the same context, however, allows the learner to increase their confidence in this distributional information. One could imagine an acquisition process, then, which on first encountering plage proceeded by a feminine article, would remain ‘‘sceptical’’ that it is in fact feminine on the basis that all other exemplars stored in the lexicon with the same ending are masculine. Subsequent exposure to other instances of the word in construction with other feminine markers would, however, gradually overcome this uncertainty. A far greater role for phonological cues could be imagined for those nouns occurring with elided definite articles such as l’escargot (‘‘the snail’’). In this case the phonological ending would indicate that it is likely to be masculine, a categorisation which would be confirmed when finding later examples of the noun in the disambiguating context un escargot. Articulating the details of such an acquisition theory, even in its most basic terms, remains well beyond the bounds of this paper. To repeat, the only particular claim being proposed is that the phonological component is likely to be sensitive to syllabic cues. One potential area of exception to the previous discussion lies with derived nominals; for example, nouns in French may be formed from verbs through the concatenation of affixes such as –ade – e.g. glisser ) glissade. Interestingly, all nouns arising from application of the same derivational affix are assigned, without exception, the same gender: feminine in the –ade case. This fact can be accounted for on the assumption that the affix is associated not only with the category feature [+N] but also with a gender feature [+fem] which is then associated with the derived form through feature percolation (Selkirk, 1982:61–2). The gender values for derived nominals, therefore, may be accounted for via rule-based processes.17 If this is so, it opens up the possibility that gender attribution may well be best accounted for in the kind of dual mechanism ‘‘words and rules’’ model argued for by Pinker (1999) and Ullman (2001).18 Some recent work by Hofmann et al. (2007) is possibly suggestive here. They report on two groups of German speaking aphasics, 16 Here, and in the l’escargot example below, we ignore the basic claim of the paper and use non-syllabic endings simply for illustrative ease of presentation. 17 One of the anonymous reviewers notes that the ‘‘bulletproof cue’’ from derivational morphology is a widespread and possibly universal property. For a close parallel in Russian see Nesset (2003). 18 This is contrary to Pinker’s (1999:262) own claim that French gender is purely a question of lexical memory. Although he cites evidence from Williams Syndrome children (Karmiloff-Smith et al., 1997; see also Monnery et al., 2002) suggesting that their poor performance in guessing the gender of nonce items is due to their problems in establishing word associations in memory, their responses to derived nominals was not explored. It might be expected that since there is evidence that rule-based systems remain intact in Williams Syndrome (Clahsen and Almazan, 1998), their performance with derived nominals may be significantly better than with non-derived instances—a possibility which has not yet been explored. Note that on Ullman’s (2001) account only fully productive morphological processes are computed by rule system. Given that the derivational processes under consideration here are only partially productive, it remains possible that they are also memory based.

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one having difficulty in determining the correctness of agreement between a noun and adjective when the noun’s gender is determined phonologically, the other having difficulty with derived nouns. This is only weak evidence, however, given that no differences were found when the participants were required to produce a definite article to agree with a presented noun and the authors themselves certainly do not interpret their results within the words and rules framework. Rodina (2007) does discuss her data within such a framework and finds some evidence for it although her main concern is with semantic cues. Of course, especially in the English past tense debate (see Pinker, 1999; Pinker and Ullman, 2002, for overviews), the adoption of dual mechanism accounts is highly controversial for those who advocate single mechanism models, either in terms of analogising processes (as in connectionist or instance-based models) or say the multiple stochastic rules of Albright and Hayes (2003). The adoption of an instance-based model in the simulations was mainly driven by the need for a precise, empirical means of measuring the value of different phonological cues. However, as noted above, it was also used because it more closely fits with psycholinguistic assumptions about the storage of gender values. What role might we assume for analogical reasoning in gender processing? With respect to acquisition it cannot be used to bootstrap the system since this requires not only that some instances are already categorised but also that the system knows in what form to represent the instances so as to aid future classification. With respect to comprehension and production, if gender value retrieval is a look-up process, then the main role for analogising is only when a noun’s value is unknown or, at least, is uncertain. If so, this is likely to be a relatively rare occurrence although it is just the kind of situation which is often tested in psycholinguistic experiments. From this perspective studies such as Tucker et al. (1977) may well tell us less about the processing of gender in the unmarked case than is usually assumed. A more general role for analogising could be imagined if lexical retrieval were sensitive to distributional properties. One indication of this, for example, might be found if the exceptions to the phonological regularities, say the previous example of plage, resulted in an increase in agreement errors. Unfortunately there is little available evidence on this matter. In fact, some writers claim that native speakers ‘‘typically make few or no mistakes’’ (Corbett, 1991:7) in this area although it is hard to judge the veracity of this statement. Certainly some have reported occasional errors (e.g. Barbaud et al., 1982; Cornish, 1994; Arnaud, 1999) although whether these are the result of incorrect gender assignment or some mistake in the agreement process is unclear. Further, since speech error corpora do not typically specify the overall sample of utterances from which the errors have been drawn, there is at present no way of quantifying the frequency of such errors (Schriefers and Jescheniak, 1999:582–83). The choice of an instance-based model was also made with an implicit eye to comparing its performance against connectionist alternatives. As discussed in section 1, both types of model appear well-suited to handling the kind of statistical regularities under investigation and as Matthews (2005) has shown with respect to classificatory accuracy in this domain both models are capable of producing results which statistically are not significantly different. That study, however, failed to consider more fine-grained performance indicators. One possible point of comparison is provided by the type of ‘‘in vivo’’ cluster analyses found in Matthews (1999b). The dendogram in Fig. 6 is based on the output activations of the four hidden units of a network trained to 100% accuracy on a subset of feminine (phonemically (not syllabically) represented and justified to the right). The activation values for each example of the set produces a matrix to which a hierarchical clustering technique can be applied which results in two elements being grouped in a cluster when they have the closest values of all elements available (Everitt, 1993). In other words, Fig. 6 shows the degree to which the network treats individual members of the set as similar. In some cases the results are as expected: for example, classe (‘‘class’’), glace (‘‘ice’’) and grace (‘‘grace’’), all ending in /as/, form a single cluster. Similarly, gloire (‘‘glory’’), histoire (‘‘story’’) and victoire (‘‘victory’’) (all ending /war/) are grouped together in a tight cluster. This latter grouping, however, indicates that the network’s classification is not quite as expected since it also includes affiche (‘‘poster’’, /afi /) and boite (‘‘box’’, /bwat/) and although the latter shares two phonemes with the /war/ group, the former shares no features in any of the corresponding feature positions. In addition some expected cluster patterns are surprisingly absent – for instance, the identite´ (‘‘identity’’), ve´rite´ (‘‘truth’’), re´alite´ (‘‘reality’’) subset are only jointly members of the largest cluster of all – whilst other clusters do not appear to have anything in common – consider the affaire (‘‘matter’’), vogue (‘‘fashion’’), nappe (‘‘tablecloth’’) and poche (‘‘pocket’’) set at the top of the dendogram. So, although the network correctly assigns gender values to a reasonably large number of nouns, it has not organised itself around the dataset in expected ways. On the basis of this type of analysis it is clear that Holmes and Segui’s (2004:428–9) claim that connectionist models support an association between word ending and gender value is too strong, at least in terms of the cited models.

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Fig. 6. Dendogram (Euclidean distance) for a subset of feminine nouns.

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Fig. 7. ‘‘Variegated’’ feature similarities for ve´rite´’s nearest neighbour set.

With respect to TiMBL an inspection of the set of nearest neighbours (NN) associated with each test word suffices to show which are being treated as similar. As a point of comparison with the previous cluster analysis, only the NN sets produced using the phonological, right-justified data representation will be reported here. As with the neural net, the classe–glace–grace group are treated similarly in the sense that each appears in the other’s analogical set. With respect to histoire and victoire (the dataset did not include gloire) both are included as the NNs of the other; also included is square (‘‘public garden’’, /skwar/) but, unlike the neural network, neither affiche nor boite are included. In further contrast to the network is TiMBL’s treatment of the –ite´ group of nouns. Although the dataset did not include identite´ or re´alite´, ve´rite´’s NN set included other –ite´ nouns qualite´ (‘‘quality’’) and quantite´ (‘‘quantity’’). Naturally given their total lack of feature overlap, none of affaire–vogue–nappe–poche group appears in the analogical sets of the others. From this brief description it appears that TiMBL partitions the data in ways which more closely accords with our expectations. These appearances are, however, slightly deceptive. Without any form of feature weighting, all feature positions are of equal importance when computing similarity so that what Albright and Hayes (2003:121) call ‘‘variegated similarity’’ may arise. Accordingly, the NN set for ve´rite´ (/veRite/) aside from qualite´ (/kalite/) and quantite´ (/ka˜tite/) also includes karate (‘‘karate’’, /kaRate/), ouvrier (‘‘workman’’, /uvRije/) and he´risson (‘‘hedgehog’’, /heRis /) since they all share three features in common with the test item as shown in Fig. 7. In other words, TiMBL accords no more importance to the end of the word than a neural network and its picking up words with similar endings is a function of the right justification of the data. Only with the application of GR weighting, which accords greater weight to the final feature positions, does TiMBL construct an NN set consisting only of qualite´ and quantite´. 7. Conclusion A noun’s grammatical gender is ultimately determined by its distributional properties and, at least in principle, the members of a particular category need have no properties in common other than their common distribution patterns. The fact that speakers are able not only to assign genders to unknown nouns but also agree on their answers (Tucker et al., 1977; Karmiloff-Smith, 1979) indicates, however, that they must share other properties. As Corbett (1991) clearly shows across a wide range of languages these properties may be semantic and/or formal (phonological and/or morphological). It may even be that the correct learning of the agreement patterns depends upon the data possessing these properties (Braine, 1987; Brooks et al., 1993; Frigo and McDonald, 1998). Part of the typological interest in gender lies in how languages differ quite significantly, not only with respect to the particular semantic or formal properties they utilise, but also in the interplay between the different systems. As a consequence it is not always possible to generalise findings from one language to another; the fact that nouns ending in an accented vowel in Qafar (East Cushitic) are feminine (Corbett, 1991:51) is of no significance, for instance, to French. With respect to phonological cues the one feature which seems to be universal is that whatever the relevant properties are they are associated with the ‘‘ending’’ of the noun.19 This paper has tried to establish that, at least for 19

There have been occasional claims that the beginning of the noun may also have an effect (e.g. Ko¨pcke and Zubin, 1984; Tucker et al., 1977) although what the status of these claims is unclear. For example, Tucker et al. (1977:25) simply note that certain initial sequences ‘‘strengthen or weaken the effects of a particular ending’’ (emphasis added) although it is worth noting that their reported results do not always corroborate this statement. Further, their categorisation of initial syllables as being ‘‘masculine’’ or ‘‘feminine’’ is based on very low numbers of examples.

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French, ‘‘ending’’ may be taken to mean ‘‘final syllable’’. Whether this applies to other languages remains to be determined, although it does appear to be consistent at least with the data presented in Corbett (1991).20 This paper has not claimed that phonological cues are the only means of determining an unknown noun’s gender. It is assumed that this is the result of a variety of cues: distributional, semantic, phonological and morphological. Quite how these different sources of information are manipulated by the child learner currently remains unclear but the results presented here are a partial step towards a more explicit model. A major difficulty lies in devising a learning mechanism powerful enough to account for the acquisition of any gendered language but not so powerful as to overwhelm an unconstrained learner. Acknowledgements Thanks for advice and suggestions at various stages to Gavin Cawley, Paul Chilton, Richard Harvey, Ken Lodge, Peter Moffatt and the valuable comments by the anonymous reviewers. Especial thanks to Andrew Boswell for sorting out the initial UNIX installation of TiMBL for me in the days before the Windows version was available. All errors naturally remain the author’s responsibility. References Aha, D., 1997. Lazy learning: special issue editorial. Artificial Intelligence Review 11, 7–10. Aha, D., Kibler, D., Albert, M., 1991. Instance-based learning algorithms. Machine Learning 6, 37–66. Albright, A., Hayes, B., 2003. Rules vs. analogy in English past tenses: a computational/experimental study. Cognition 90, 119–161. Arnaud, P., 1999. Target-error resemblance in French word substitution speech errors and the mental lexicon. Applied Psycholinguistics 20, 269– 287. Arndt, W., 1970. Nonrandom assignments of loan words: German noun gender. Word 26, 244–253. 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For example, the assignment rules for Qafar (Corbett, 1991:51) could simply be stated as ‘‘Nouns whose a final syllable contains an accented vowel are feminine; otherwise masculine’’.

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