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Abstract

This paper focuses on the evaluation of some methods for the automatic acquisition of Multiword Expressions (MWEs). First we investigate the hypothesis that MWEs can be detected solely by the distinct statistical properties of their component words, regardless of their type, comparing 3 statistical measures: Mutual Information, Chi**2 and Permutation Entropy. Moreover, we also look at the impact that the addition of type-specific linguistic information has on the performance of these methods.

Type

a

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Abstract

We are in 2013, and multiword expressions have been around for a while in the computational linguistics research community. Since the first ACL workshop on MWEs 12 years ago in Sapporo, Japan, much has been discussed, proposed, experimented, evaluated and argued about MWEs. And yet, they deserve the publication of a whole special issue of the ACM Transactions on Speech and Language Processing. But what is it about multiword expressions that keeps them in fashion? Who are the people and the institutions who perform and publish groundbreaking fundamental and applied research in this field? What is the place and the relevance of our lively research community in the bigger picture of computational linguistics? Where do we come from as a community, and most importantly, where are we heading? In this introductory article, we share our point of view about the answers to these questions and introduce the articles that compose the current special issue.

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Abstract

This book is an excellent introduction to multiword expressions. It provides a unique, comprehensive and up-to-date overview of this exciting topic in computational linguistics. The first part describes the diversity and richness of multiword expressions, including many examples in several languages. These constructions are not only complex and arbitrary, but also much more frequent than one would guess, making them a real nightmare for natural language processing applications. The second part introduces a new generic framework for automatic acquisition of multiword expressions from texts. Furthermore, it describes the accompanying free software tool, the mwetoolkit, which comes in handy when looking for expressions in texts (regardless of the language). Evaluation is greatly emphasized, underlining the fact that results depend on parameters like corpus size, language, MWE type, etc. The last part contains solid experimental results and evaluates the mwetoolkit, demonstrating its usefulness for computer-assisted lexicography and machine translation. This is the first book to cover the whole pipeline of multiword expression acquisition in a single volume. It is addresses the needs of students and researchers in computational and theoretical linguistics, cognitive sciences, artificial intelligence and computer science. Its good balance between computational and linguistic views make it the perfect starting point for anyone interested in multiword expressions, language and text processing in general.

Content

1.Introduction.- Part I.Multiword Expressions: a Tough Nut to Crack.- 2.Definitions and Characteristics.- 3 State of the Art in MWE Processing.- Part II.MWE Acquisition.- 4.Evaluation of MWE Acquisition.- 5.A New Framework for MWE Acquisition.- Part III Applications.- 6.Application 1: Lexicography.- 7.Application 2: Machine Translation.- 8.Conclusions.- Appendixes.- A.Extended List of Translation Examples.- B.Resources Used in the Experiments.- C.The mwetoolkit: Documentation.- D.Tagsets for POS and syntax.- E.Detailed Lexicon Descriptions.