Semantic Domains in Computational Linguistics

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1 Semantic Domains in Computational Linguistics

2 Alfio Gliozzo Carlo Strapparava Semantic Domains in Computational Linguistics

3 Dr. Alfio Gliozzo FBK-irst Via Sommarive Povo-Trento Italy Dr. Carlo Strapparava FBK-irst Via Sommarive Povo-Trento Italy ISBN e-isbn DOI / Springer Dordrecht Heidelberg London New York Library of Congress Control Number: ACM Computing Classification (1998): 1.2.7, H.3.1, J.5 Springer-Verlag Berlin Heidelberg 2009 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: KuenkelLopka GmbH Printed on acid-free paper Springer is part of Springer Science+Business Media (

4 Preface Ambiguity and variability are two basic and pervasive phenomena characterizing lexical semantics. In this book we introduce a computational model for lexical semantics based on Semantic Domains. This concept is inspired by the Theory of Semantic Fields, proposed in structural linguistics to explain lexical semantics. The main property of Semantic Domains is lexical coherence, i.e. the property of domain-related words to co-occur in texts. This allows us to define automatic acquisition procedures for Domain Models from corpora, and the acquired models provide a shallow representation for lexical ambiguity and variability. Domain Models have been used to define a similarity metric among texts and terms in the Domain Space, where second-order relations are reflected. Topic similarity estimation is at the basis of text comprehension, allowing us to define a very general domain-driven methodology. The basic argument we put forward to support our approach is that the information provided by the Domain Models can be profitably used to boost the performances of supervised Natural Language Processing systems for many tasks. In fact, Semantic Domains allows us to extract domain features for texts, terms and concepts. The obtained indexing, adopted by the Domain Kernel to estimate topic similarity, preserves the original information while reducing the dimensionality of the feature space. The Domain Kernel is used to define a semi-supervised learning algorithm for Text Categorization that achieves state-of-the-art results while decreasing by one order the quantity of labeled texts required for learning. The property of the Domain Space to represent together terms and texts allows us to define an Intensional Learning schema for Text Categorization, in which categories are described by means of discriminative words instead of labeled examples, achieving performances close to human agreement. Then we investigate the role of domain information in Word Sense Disambiguation, developing both unsupervised and supervised approaches that strongly rely on the notion of Semantic Domain. The former is based on the lexical resource WordNet Domains and the latter exploits both sense tagged and unlabeled data to model the relevant domain distinctions among word senses. The proposed supervised approach improves the

5 VI Preface state-of-the-art performance in many tasks for different languages, while reducing appreciably the amount of sense tagged data required for learning. Finally, we present a lexical acquisition procedure to obtain Multilingual Domain Models from comparable corpora. We exploit such models to approach a Cross-language Text Categorization task, achieving very promising results. We would first of all acknowledge the effort of other people involved in the eight years long daily work required to produce the experimental results reported in this monograph, and in particular Claudio Giuliano, who performed most of the experimental work for the WSD experiments, allowing us to achieve very accurate results in competitions due to his patience and skills; to Bernardo Magnini, who first proposed the concept of Semantic Domain, opening the direction we have followed during our research path and supporting it with financial contributions from his projects; and to Ido Dagan, who greatly contributed to the intensional learning framework defining the experimental settings and clarifying the statistical properties of the GM algorithm. Special thanks are devoted to Oliviero Stock, for his daily encouragement and for the appreciation he has shown for our work; to Walter Daelemans, who demonstrated a real interest in the epistemological aspects of this work from the early stages; to Maurizio Matteuzzi, whose contribution was crucial to interpret the theoretical background of this work related to philosophy of language; to Roberto Basili who immediately understood the potential of Semantic Domains and creatively applied our framework for technology transfer, contributing to highlighting limitations and potentialities; and to Aldo Gangemi, who more recently helped us in clarifying the relationship of this work with formal semantics and knowledge representation. Last, but not least, we would like to thank our families and parents for having understood with patience our crazy lives, and our friends for having spent their nights in esoteric and sympathetic discussions. Trento, September 2008 Alfio Gliozzo Carlo Strapparava

6 Contents 1 Introduction Lexical Semantics and Text Understanding Semantic Domains: Computational Models for Lexical Semantics Structure of the Book Semantic Domains Domain Models Semantic Domains in Text Categorization Semantic Domains in Word Sense Disambiguation Multilingual Domain Models Kernel Methods for Natural Language Processing Semantic Domains The Theory of Semantic Fields Semantic Fields and the meaning-is-use View Semantic Domains The Domain Set WordNet Domains Lexical Coherence: A Bridge from the Lexicon to the Texts Computational Models for Semantic Domains Domain Models Domain Models: Definition The Vector Space Model The Domain Space WordNet-Based Domain Models Corpus-Based Acquisition of Domain Models Latent Semantic Analysis for Term Clustering The Domain Kernel Domain Features in Supervised Learning The Domain Kernel

7 VIII Contents 4 Semantic Domains in Text Categorization Domain Kernels for Text Categorization Semi-supervised Learning in Text Categorization Evaluation Discussion Intensional Learning Intensional Learning for Text Categorization Domain Models and the Gaussian Mixture Algorithm for Intensional Learning Evaluation Discussion Summary Semantic Domains in Word Sense Disambiguation The Word Sense Disambiguation Task The Knowledge Acquisition Bottleneck in Supervised WSD Semantic Domains in the WSD Literature Domain-Driven Disambiguation Methodology Evaluation Domain Kernels for WSD The Domain Kernel Syntagmatic Kernels WSD Kernels Evaluation Discussion Multilingual Domain Models Multilingual Domain Models: Definition Comparable Corpora Cross-language Text Categorization The Multilingual Vector Space Model The Multilingual Domain Kernel Automatic Acquisition of Multilingual Domain Models Evaluation Implementation Details Monolingual Text Categorization Results Cross-language Text Categorization Results Summary Conclusion and Perspectives for Future Research Summary Future Work Consolidation of the Present Work Domain-Driven Technologies

8 Contents IX 7.3 Conclusion A Appendix: Kernel Methods for NLP A.1 Supervised Learning A.2 Feature-Based vs. Instance-Based Learning A.3 Linear Classifiers A.4 Kernel Methods A.5 Kernel Functions A.6 Kernels for Text Processing References

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