Word Sense Disambiguation

1. Front Matter

   Pages i-xxii

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2. Introduction

   • Eneko Agirre, Philip Edmonds

   Pages 1-28

3. Word Senses

   • Adam Kilgarriff

   Pages 29-46

4. Making Sense About Sense

   • Nancy Ide, Yorick Wilks

   Pages 47-73

5. Evaluation of WSD Systems

   • Martha Palmer, Hwee Tou Ng, Hoa Trang Dang

   Pages 75-106

6. Knowledge-Based Methods for WSD

   • Rada Mihalcea

   Pages 107-131

7. Unsupervised Corpus-Based Methods for WSD

   • Ted Pedersen

   Pages 133-166

8. Supervised Corpus-Based Methods for WSD

   • Lluís Màrquez, Gerard Escudero, David Martínez, German Rigau

   Pages 167-216

9. Knowledge Sources for WSD

   • Eneko Agirre, Mark Stevenson

   Pages 217-251

10. Automatic Acquisition of Lexical Information and Examples

    • Julio Gonzalo, Felisa Verdejo

    Pages 253-274

11. Domain-Specific WSD

    • Paul Buitelaar, Bernardo Magnini, Carlo Strapparava, Piek Vossen

    Pages 275-298

12. WSD in NLP Applications

    • Philip Resnik

    Pages 299-337

13. Back Matter

    Pages 339-366

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Graeme Hirst University of Toronto Of the many kinds of ambiguity in language, the two that have received the most attention in computational linguistics are those of word senses and those of syntactic structure, and the reasons for this are clear: these ambiguities are overt, their resolution is seemingly essential for any prac- cal application, and they seem to require a wide variety of methods and knowledge-sources with no pattern apparent in what any particular - stance requires. Right at the birth of artificial intelligence, in his 1950 paper “Computing machinery and intelligence”, Alan Turing saw the ability to understand language as an essential test of intelligence, and an essential test of l- guage understanding was an ability to disambiguate; his example involved deciding between the generic and specific readings of the phrase a winter’s day. The first generations of AI researchers found it easy to construct - amples of ambiguities whose resolution seemed to require vast knowledge and deep understanding of the world and complex inference on this kno- edge; for example, Pharmacists dispense with accuracy. The disambig- tion problem was, in a way, nothing less than the artificial intelligence problem itself. No use was seen for a disambiguation method that was less than 100% perfect; either it worked or it didn’t. Lexical resources, such as they were, were considered secondary to non-linguistic common-sense knowledge of the world.

• algorithms
• artificial intelligence
• computational linguistics
• language
• linguistics

From the reviews:

"This collection serves as a thorough record of where we are now and provides some nice pointers for where we need to go. It is a great resource containing valuable reference material, helpful summaries of findings, further-reading sections, and a useful appendix on resources… Even though the book is tailored for those new to the field, veteran WSD researchers will find the collection makes good reading with plenty of material and discussions that do not appear elsewhere." (Diana McCarthy, COMPUTATIONAL LINGUISTICS, volume 33, number 2)

• Department of Computer Science, University of the Basque Country, Donostia, Spain

  Eneko Agirre

• Sharp Laboratories of Europe Limited, Oxford Science Park, UK

  Philip Edmonds