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Application of natural language to information systems (NLDB'14)
Data & Knowledge Engineering 100 (2015) 189–190
Contents lists available at ScienceDirect
Data & Knowledge Engineering journal homepage: www.elsevier.com/locate/datak
Preface
Application of natural language to information systems (NLDB'14)
This special issue consists of four revised and extended papers selected from the 19th International Conference on Applications of Natural Language to Information Systems (NLDB 2014), which was organized in June 2014 in Montpellier, France. For nearly 20 years, NLDB conferences (http://nldb.org) bring together researchers interested in the use of natural language processing techniques in applications such as database querying, information systems architecture, software development, specification checking, and more recently, various applications dealing with automatic enrichment of and advanced search in large volumes of web content, including opinion mining and sentiment analysis. Authors of a selected number of contributions to the conference (13 full papers, 8 short papers, and 14 poster presentations, among the 73 submitted ones) were asked to develop their papers into journal articles. After additional reviewing, 4 papers were eventually selected for this special issue. The addressed topics (e.g, information retrieval, information extraction, reasoning, disambiguation) underline the various and complex issues addressed by the Natural Language Processing (NLP) and Information Systems (IS) communities. More precisely, the research topics of these papers are summarized in the following: A Novel Methodology for Retrieving Infographics Utilizing Structure and Message by Zhuo Li, Sandra Carberry, Hui Fang, Ph.D., Kathleen F McCoy, Kelly Peterson, and Matthew Stagitis Information graphics represent an effective visual representation of relationships between data entities. This paper proposes a novel methodology for infographics retrieval based on two fundamental aspects: graphic's structural and message content. The original methodology presented by the authors is based on 3 steps: query processing, graphic preprocessing, and ranking in order to measure the relevance of infographics to the queries. This paper highlights crucial characteristics of infographics, i.e. their two-dimensional structure and the high-level intended message that their graphic designer intended to convey through specific communicative signals. A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases by André Freitas, Joao Carlos P da Silva, Edward Curry, and Paul Buitelaar This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model, which can be applied to querying and reasoning over heterogeneous knowledge bases (KBs). First, the approach provides a semantic selection mechanism for facts, which are relevant and meaningful in a specific querying and reasoning context. Second,, the proposed method enables to take into account information incompleteness in large KBs. Extraction and Clustering of Arguing Expressions in Contentious Text by Amine Trabelsi and Osmar R Zaiane This paper addresses opinion-mining issues in order to enhance the quality of analysis of contentious texts. More precisely, the authors present the Joint Topic Viewpoint (JTV), probabilistic model for analyzing the underlying divergent arguing expressions. Latent Dirichlet Allocation (LDA) method is a very popular topic modeling approach. But LDA fails to deal with more complex structures of texts like contention where viewpoints are hidden. In this context, the authors propose a new method that considers a contentious document as a pair of dependent mixtures: a mixture of arguing topics and a mixture of viewpoints for each topic. The qualitative and quantitative assessments of the proposed approaches show a good capacity of JTV in handling different contentious issues. A Hybrid Possibilistic Approach for Arabic Full Morphological Disambiguation by Ibrahim Bounhas, Raja Ayed, Bilel Elayeb, and Narjès Bellamine Ben Saoud This paper deals with the disambiguation of the morphological features of non-vocalized Arabic texts. A word is considered as ambiguous if at least one of its morphological features has several possible values. New approaches are proposed by combining
http://dx.doi.org/10.1016/j.datak.2015.07.001 0169-023X/© 2015 Elsevier B.V. All rights reserved.
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Preface
statistical information (e.g. information gain) and linguistic rules to disambiguate and to reduce the number of candidates. So these different methods represent a complete system for Arabic morphological analysis and disambiguation. On behalf of the authors of the selected papers, we would like to thank all reviewers for their detailed and constructive comments on the submitted manuscripts. We also wish to thank Dr Peter Chen, Hilda Xu, and all the efficient Elsiever team for their support in the elaboration of this special issue. It is hoped that this DKE issue will make a good reference material and be of great use for the NLP and IS community. Elisabeth Métais Conservatoire National des Arts et Métiers (CNAM), Paris, France Corresponding author at: Conservatoire National des Arts et Métiers, 2 rue Conte, 75003 Paris, France. Tel.: +33 1 40 27 29 08. E-mail address: [email protected]. Mathieu Roche La Recherche Agronomique pour le Développement (Cirad), Montpellier, France E-mail address: [email protected]. Maguelonne Teisseire Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (Irstea), Montpellier, France E-mail address: [email protected].
Contents lists available at ScienceDirect
Data & Knowledge Engineering journal homepage: www.elsevier.com/locate/datak
Preface
Application of natural language to information systems (NLDB'14)
This special issue consists of four revised and extended papers selected from the 19th International Conference on Applications of Natural Language to Information Systems (NLDB 2014), which was organized in June 2014 in Montpellier, France. For nearly 20 years, NLDB conferences (http://nldb.org) bring together researchers interested in the use of natural language processing techniques in applications such as database querying, information systems architecture, software development, specification checking, and more recently, various applications dealing with automatic enrichment of and advanced search in large volumes of web content, including opinion mining and sentiment analysis. Authors of a selected number of contributions to the conference (13 full papers, 8 short papers, and 14 poster presentations, among the 73 submitted ones) were asked to develop their papers into journal articles. After additional reviewing, 4 papers were eventually selected for this special issue. The addressed topics (e.g, information retrieval, information extraction, reasoning, disambiguation) underline the various and complex issues addressed by the Natural Language Processing (NLP) and Information Systems (IS) communities. More precisely, the research topics of these papers are summarized in the following: A Novel Methodology for Retrieving Infographics Utilizing Structure and Message by Zhuo Li, Sandra Carberry, Hui Fang, Ph.D., Kathleen F McCoy, Kelly Peterson, and Matthew Stagitis Information graphics represent an effective visual representation of relationships between data entities. This paper proposes a novel methodology for infographics retrieval based on two fundamental aspects: graphic's structural and message content. The original methodology presented by the authors is based on 3 steps: query processing, graphic preprocessing, and ranking in order to measure the relevance of infographics to the queries. This paper highlights crucial characteristics of infographics, i.e. their two-dimensional structure and the high-level intended message that their graphic designer intended to convey through specific communicative signals. A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases by André Freitas, Joao Carlos P da Silva, Edward Curry, and Paul Buitelaar This paper proposes a selective graph navigation mechanism based on a distributional relational semantic model, which can be applied to querying and reasoning over heterogeneous knowledge bases (KBs). First, the approach provides a semantic selection mechanism for facts, which are relevant and meaningful in a specific querying and reasoning context. Second,, the proposed method enables to take into account information incompleteness in large KBs. Extraction and Clustering of Arguing Expressions in Contentious Text by Amine Trabelsi and Osmar R Zaiane This paper addresses opinion-mining issues in order to enhance the quality of analysis of contentious texts. More precisely, the authors present the Joint Topic Viewpoint (JTV), probabilistic model for analyzing the underlying divergent arguing expressions. Latent Dirichlet Allocation (LDA) method is a very popular topic modeling approach. But LDA fails to deal with more complex structures of texts like contention where viewpoints are hidden. In this context, the authors propose a new method that considers a contentious document as a pair of dependent mixtures: a mixture of arguing topics and a mixture of viewpoints for each topic. The qualitative and quantitative assessments of the proposed approaches show a good capacity of JTV in handling different contentious issues. A Hybrid Possibilistic Approach for Arabic Full Morphological Disambiguation by Ibrahim Bounhas, Raja Ayed, Bilel Elayeb, and Narjès Bellamine Ben Saoud This paper deals with the disambiguation of the morphological features of non-vocalized Arabic texts. A word is considered as ambiguous if at least one of its morphological features has several possible values. New approaches are proposed by combining
http://dx.doi.org/10.1016/j.datak.2015.07.001 0169-023X/© 2015 Elsevier B.V. All rights reserved.
190
Preface
statistical information (e.g. information gain) and linguistic rules to disambiguate and to reduce the number of candidates. So these different methods represent a complete system for Arabic morphological analysis and disambiguation. On behalf of the authors of the selected papers, we would like to thank all reviewers for their detailed and constructive comments on the submitted manuscripts. We also wish to thank Dr Peter Chen, Hilda Xu, and all the efficient Elsiever team for their support in the elaboration of this special issue. It is hoped that this DKE issue will make a good reference material and be of great use for the NLP and IS community. Elisabeth Métais Conservatoire National des Arts et Métiers (CNAM), Paris, France Corresponding author at: Conservatoire National des Arts et Métiers, 2 rue Conte, 75003 Paris, France. Tel.: +33 1 40 27 29 08. E-mail address: [email protected]. Mathieu Roche La Recherche Agronomique pour le Développement (Cirad), Montpellier, France E-mail address: [email protected]. Maguelonne Teisseire Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (Irstea), Montpellier, France E-mail address: [email protected].