Nicoletta Calzolari

DETECTING PATTERNS IN A LEXICAL DATA BASE

In a well-structured Lexical Data Base, a number of relations among lexical entries can be interactively evidenced. The present article examines hyponymy, as an example of paradigmatic relation, and restriction relation, as a syntagmatic relation. The theoretical results of their implementation are illustrated.

Acquisition of lexical information: from a large textual Italian corpus

The creation and development of a large Lexical Database (LDB) which, until now, mainly reuses the data found in standard Machine Readable Dictionaries, has been going on in Pisa for a number of years (see Calzolari 1984, 1988, Calzolari, Picchi 1988). We are well aware that, in order to build a more powel-ful I.DB (or even a Le.dcal Knowledge Base) to be used in different ComputationM l.inguistics (CL) applications, types of information other than those usually found in machine readable dictionaries are urgently needed. Different sources of information must therefore be exploited if we wemt to overcome the qchcal bottleneck ~ of Natural l..anguage Processing ( N I P ) . In a trend which is becoming increasingly relevant both m c1 proper and in Literao and Iinguistic Computing, we feel that very interesting data ibr our LI)Bs c:m be found b.v processing large textuM corpora, where the actual usage of the language can be truly investigated. Many research projects are nowadays collecting large amounts of textuM data, thus providing more and more material to be analyzed for descriptions based on measurable evidence of how language is actually used. We uhhnately aim at integrating lexical data extracted from the an,dysis of large textual corpora into the I,DB we are implementing. These data refer, typically, to: i) complementation relations introduced by prepositions (e.g. dividere subcategorizes for a PP headed by the preposition in ha one sense, and by the preposition fra in another sense); ii) lexically conditioned modification relations (tena macchina potente , un farmaco potente and not /brte , while un cajfe forte , una moneta forte and not potente ); iii) lefically significant collocations (premiere ut~a decisione and not fare z~na decisione , prestare attenzione and not dare ); iv) fixed phrases and idioms I (donna itz carriera, dottorato di ricerca, a propo~ito di); v) compounds ( tarola calda, ~ave scuo/a). All these types of data are a major issue of practical relevance, and particularly problematic, in many N I P applications in different areas. They should therefore be dvcn very lmge coverage in any useful LDB, and, moreover, should also be annotated, in a computerk,ed lexicon, for the pe~inent t)equency information obtained fiom the processed corpus, and obviously updated flom time to time. As a matter of fact, dictionaries now tend to encode all the theoreticcd possibilities on a same level, but if e ; e~ possibility in the diction:m, must be given equal weight, parsing is very diificult (Church 1988, p.3): they should provide infornaation on what is more likely to occur, e.g. relative likelihood of alternate pm-ts of speech for a word or of ahernate word-senses, both out of context and it possible taking into account contextu~d factors. Statistical anMyses of linguistic data were very popular in the 50s and 60s, mainly, even though not only, for literary types of analyses and for studies on the lexicon (Guiraud 1959, Muller 1964, Moskovich 1977). Stochastic approaches to linguistic analyses have been strongly reevaluated in the past few years, either for syntactic analysis (Gmside et al. 1987, Church 1988), or for NLP applications (Brown et al. 1988), or for semantic analysis (Zemik 1989, Smadja 1989). Quantitative (not statistical) evidence on e.g. word-sense occurrences in a large corpus have been taken into account for lexicographic descriptions (Cobuild 1%7).

Current Issues in Computational Linguistics: In Honour of Don Walker

Introduction. Donald Walker: a Remembrance. Section 1: The Task of Natural Language Processing. Natural Language Processing: an Historical Review K. Sparck Jones. On Getting a Computer to Listen J. Robinson. Utterance and Objective: Issues in Natural Language Communication B. Grosz. On the Proper Place of Semantics in Machine Translation M. King. Developing a Natural Language Interface to Complex Data G.G. Hendrix, E.D. Sacerdoti. User-Needs Analysis and Design Methodology for an Automated Document Generator K. Kukich, K. McKeown, J. Shaw, J. Robin, J. Lim, N. Morgan, J. Philips. Section 2: Building Computational Lexicons. Machine-Readable Dictionaries and Computational Linguistics Research B. Boguraev. Research Toward the Development of a Lexical Knowledge Base for Natural Language Processing R.A. Amsler. Discovering Relationships Among Word Senses R.J. Byrd. Machine Readable Dictionary as a Source of Grammatical Information E. Hajicova, A. Rosen. The ITT Lexical Database: Dream and Reality S. Pin-Ngern Conlon. Visions of the Digital Library: Views on Using Computational Linguistics and Semantic Nets in Information Retrieval J.L. Klavans. Anatomy of a Verb Entry: from Linguistic Theory to Lexicographic Practice B.T. Atkins, J. Kegl, B. Levin. Issues for Lexicon Building N. Calzolari. Outline of a Model for Lexical Databases N. Ide, J. le Maitre, J. Veronts. Construction-Based MT Lexicons L. Levin, S. Nirenburg. Dependency-Based Grammatical Information in the Lexicon P. Sgall. Semantics in the Brains Lexicon -- Some Preliminary Remarks on its Epistemology H. Schnelle. Section 3: The Acquisition and Use of LargeCorpora. The Ecology of Language D.E. Walker. Representativeness in Corpus Design D. Biber. The Text Encoding Initiative C.M. Sperberg-McQueen. Discrimination Decisions for 100,000 Dimensional W.A. Gale, K.W. Church, D. Yarowsky. Acquisition and Exploitation of Textual Resources for NLP S. Armstrong-Warwick. The Center for Electronic Texts in the Humanities S. Hockey. Design Principles for Electronic Textual Resources: Investigating Users and Uses of Scholarly Information N.J. Belkin. Section 4: Topics, Methods and Formalisms in Syntax, Semantics and Pragmatics. Evaluating English Sentences in a Logical Model J. Friedman, D.B. Moran, D.S. Warren. Recovering Implicit Information M.S. Palmer, D.A. Dahl, R.J. Schiffman, L. Hirschman, M. Linebarger, J. Downing. Flexible Generation: Taking the User into Account C.L. Paris, V.O. Mittal. Two Principles of Parse Preference J.R. Hobbs, J. Bear. UD, yet Another Unification Device R. Johnson, M. Rosner. Varieties of Heuristics in Sentence Parsing M. Nagao. Some Recent Trends in Natural Language Processing A.K. Joshi. Stone Soup and the French Room Y. Wilks.

Towards the organization of lexical definitions on a database structure

P r i n t e d d i c t i o n a r i e s a re g r e s t r e p o s i t o r i e s of i n f o r m a t i o n , and i t i s i m p o r t a n t t h a t they can be e x p l o i t e d as f u l l ~ as p o s s i b l e , w i t h r e g a r d to a l l the d i f f e r e n t t ype s of d a t a they c o n t a i n . This was one of the aims when o r g a n i z i n g the Machine D£otionax7 of the I t a l i a n l anguage on a d a t a b a s e s t r u c t u r e .

Automatic acquisition of lexico-semantic knowledge for question answering

Lexico-semantic knowledge is becoming increasingly important within the area of natural language processing, especially for applications, such as Word Sense Disambiguation, Information Extraction and Question Answering (QA). Although the coverage of handmade resources, such as WordNet (Fellbaum, 1998), in general is impressive, coverage problems still exist for those applications involving specific domains or languages other than English. We are interested in using lexico-semantic knowledge in an open-domain question answering system for Dutch. Obtaining such knowledge from existing resources is possible, but only to a certain extent. The most important resource for our research is the Dutch portion of EuroWord-Net (Vossen, 1998), however its size is only half of that of the English WordNet. Therefore, many of the lexical items used in the QA task of the Cross Language Evaluation Forum (CLEF 1) for Dutch cannot be found in EuroWordNet. In addition, information regarding the classes to which named entities belong, e.g. Narvik IS-A harbour, has been shown to be useful for QA, but such information is typically absent from hand-built resources. For these reasons, we are interested in investigating methods which acquire lexico-semantic knowledge automatically from text corpora.

Standardization in the Lexicon

Lexicons, as described in the previous chapter, are a valuable resource, not only for wordclass tagging but also for many other applications in the broad area of language engineering (LE), which encompasses fields such as computational linguistics and Natural Language Processing (NLP). Furthermore, the last decade in particular has seen an increasing use of corpora for computational lexicography, other corpus-based research and development of applications, all of which has led to the general recognition of the value of ‘authentic’ data.

Lexicons, Terminologies, Ontologies: Reflections from Experiences in Resource Construction

This contribution, aims at highlighting the strong interconnection between lexicons, terminologies and ontologies and especially the fundamental role that ontologies and lexica mutually play. Our view is that lexical resources are evolving in nature, from ontologically based lexicons we are going towards lexically based ontologies. We explore different instantiations of the current trend of using formal ontologies as a core module of computational lexicons, presenting the advantages especially in multilingual and terminological contexts. We present work showing that the lexical knowledge already present in non formal computational lexicons can be exploited to derive or enrich a formal ontology without much manual effort. In the terminology domain, we describe the construction of a resource for biology, directly linked to a parallel domain-ontology, that combines characteristics of both lexicons and terminologies, so that is can allow for intelligent access to content. Finally, we describe our experience in two projects in which formal ontologies play a central role in the context of multilingual computational lexicons, where the ontology is what acts as the glue among the different monolingual lexicons and what provides cross-lingual reasoning capabilities.

Boosting Lexical Resources for the Semantic Web: Generative Lexicon and Lexicon Interoperability

To make the vision of a European Information Infrastructure and of the Semantic Web a reality, two key issues are tackled: (i) content, which must be dealt with in a multilingual environment; (ii) standards, which are critical to achieve interoperability and integration. In the Semantic Web scenario, ontologies are the key components to manage knowledge, whereas, in Human Language Technology, semantic description is committed to computational lexicons, which have to squarely address the complexity of natural language. Answers to the above issues are found within two frameworks: first, in the framework of Generative Lexicon (GL) theory and GL-based lexicons that account for the complex, multidimensional and multifaceted nature of meaning in lexicon and ontology design; second, in the context of the ISLE enterprise which, with the MILE, represents an essential interface between advanced research in the field of multilingual lexical semantics and the practical task of developing resources for HLT.

Ontology and the Lexicon: Learning and using ontological knowledge

Part I. Fundamental Aspects: 1. Ontology and the lexicon: a multi-disciplinary perspective Laurent Prevot, Chu-Ren Huang, Nicoletta Calzolari, Aldo Gangemi, Alessandro Lenci and Alessandro Oltramari 2. Formal ontology as interlingua: the SUMO and WordNet linking project and GlobalWordNet Adam Pease and Christiane Fellbaum 3. Interfacing WordNet with DOLCE: towards OntoWordNet Aldo Gangemi, Nicola Guarino, Claudio Masolo and Alessandro Oltramari 4. Reasoning over natural language text by means of FrameNet and ontologies Jan Scheffczyk, Collin F. Baker and Srini Narayanan 5. Synergizing ontologies and the lexicon: a roadmap Alessandro Oltramari, Aldo Gangemi, Chu-Ren Huang, Nicoletta Calzolari, Alessandro Lenci and Laurent Prevot Part II. Discovery and Representation of Conceptual Systems: 6. Experiments of ontology construction with formal concept analysis SuJian Li, Qin Lu and Wenjie Li 7. Ontology, lexicon, and fact repository as leveraged to interpret events of change Marjorie McShane, Sergei Nirenburg and Stephen Beale 8. Hantology: conceptual system discovery based on orthographic convention Ya-Min Chou and Chu-Ren Huang 9. Whats in a schema? A formal metamodel for ECG and FrameNet Aldo Gangemi Part III. Interfacing Ontologies and Lexical Resources: 10. Interfacing ontologies and lexical resources Laurent Prevot, Stefano Borgo and Alessandro Oltramari 11. Sinica BOW (Bilingual Ontological WordNet): integration of BilingualWord-Net and SUMO Chu-Ren Huang, Ru-Yng Chang and Hsiang-bin Lee 12. Ontology-based semantic lexicons: mapping between terms and object descriptions Paul Buitelaar 13. Merging global and specialized linguistic ontologies Manuela Speranza and Bernardo Magnini Part IV. Learning and Using Ontological Knowledge: 14. The life cycle of knowledge Alessandro Lenci 15. The omega ontology Andrew Philpot, Eduard Hovy and Patrick Pantel 16. Automatic acquisition of lexico-semantic knowledge for question answering Lonneke van der Plas, Gosse Bouma and Jori Mur 17. Agricultural ontology construction and maintenance in Thai Asanee Kawtrakul and Aurawan Imsombut.The relation between ontologies and language is at the forefront of both natural language processing (NLP) and knowledge engineering. Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to map accurately the relations between words and the concepts that they can be linked to. This book focuses on the integration of lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.

Ontology and the Lexicon: Fundamental aspects

Part I. Fundamental Aspects: 1. Ontology and the lexicon: a multi-disciplinary perspective Laurent Prevot, Chu-Ren Huang, Nicoletta Calzolari, Aldo Gangemi, Alessandro Lenci and Alessandro Oltramari 2. Formal ontology as interlingua: the SUMO and WordNet linking project and GlobalWordNet Adam Pease and Christiane Fellbaum 3. Interfacing WordNet with DOLCE: towards OntoWordNet Aldo Gangemi, Nicola Guarino, Claudio Masolo and Alessandro Oltramari 4. Reasoning over natural language text by means of FrameNet and ontologies Jan Scheffczyk, Collin F. Baker and Srini Narayanan 5. Synergizing ontologies and the lexicon: a roadmap Alessandro Oltramari, Aldo Gangemi, Chu-Ren Huang, Nicoletta Calzolari, Alessandro Lenci and Laurent Prevot Part II. Discovery and Representation of Conceptual Systems: 6. Experiments of ontology construction with formal concept analysis SuJian Li, Qin Lu and Wenjie Li 7. Ontology, lexicon, and fact repository as leveraged to interpret events of change Marjorie McShane, Sergei Nirenburg and Stephen Beale 8. Hantology: conceptual system discovery based on orthographic convention Ya-Min Chou and Chu-Ren Huang 9. Whats in a schema? A formal metamodel for ECG and FrameNet Aldo Gangemi Part III. Interfacing Ontologies and Lexical Resources: 10. Interfacing ontologies and lexical resources Laurent Prevot, Stefano Borgo and Alessandro Oltramari 11. Sinica BOW (Bilingual Ontological WordNet): integration of BilingualWord-Net and SUMO Chu-Ren Huang, Ru-Yng Chang and Hsiang-bin Lee 12. Ontology-based semantic lexicons: mapping between terms and object descriptions Paul Buitelaar 13. Merging global and specialized linguistic ontologies Manuela Speranza and Bernardo Magnini Part IV. Learning and Using Ontological Knowledge: 14. The life cycle of knowledge Alessandro Lenci 15. The omega ontology Andrew Philpot, Eduard Hovy and Patrick Pantel 16. Automatic acquisition of lexico-semantic knowledge for question answering Lonneke van der Plas, Gosse Bouma and Jori Mur 17. Agricultural ontology construction and maintenance in Thai Asanee Kawtrakul and Aurawan Imsombut.The relation between ontologies and language is at the forefront of both natural language processing (NLP) and knowledge engineering. Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to map accurately the relations between words and the concepts that they can be linked to. This book focuses on the integration of lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.