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contributors
foreword
preface
1 introduction
eneko agirre and philip edmonds
1¡£1 word sense disambiguation
1¡£2 a brief history of wsd research
1¡£3 what is a word sense?
1¡£4 applications of wsd
1¡£5 basic approaches to wsd
1¡£6 state-of-the-art performance
1¡£7 promising directions
1¡£8 overview of this bok
1¡£9 further reading
references

2 word senses
adam kilgarriff
2¡£1 introduction
2¡£2 lexicographers
2¡£3 philosophy
2¡£3¡£1 meaning is something you do
2¡£3¡£2 the fregean tradition and reification
2¡£3¡£3 two incompatible semantics?
2¡£3¡£4 implications for word senses
2¡£4 lexicalization
2¡£5 corpus evidence
2¡£5¡£1 lexicon size
2¡£5¡£2 quotations
2¡£6 conclusion
2¡£7 further reading
acknowledgments
references

3 making sense about sense
nancy ide and yorick wilks
3¡£1 introduction
3¡£2 wsd and the lexicographers
3¡£3 wsd and sense inventories
3¡£4 nlp applications and wsd
3¡£5 what level of sense distinctions do we need for nlp, if any?
3¡£6 what now for wsd?
3¡£7 conclusion
references

4 evaluation of wsd systems
martha palmer, hwee tou ng and hoa trang dang
4¡£1 introduction
4¡£1¡£1 terminology
4¡£1¡£2 overview
4¡£2 background
4¡£2¡£1 wordnet and semcor
4¡£2¡£2 the line and interest corpora
4¡£2¡£3 the dso corpus
4¡£2¡£4 open mind word expert
4¡£3 evaluation using pseudo-words
4¡£4 senseval evaluation exercises
4¡£4¡£1 senseval-1
evaluation and scoring
4¡£4¡£2 senseval-2
english all-words task
english lexical sample task
4¡£4¡£3 comparison of tagging exercises
4¡£5 sources of inter-annotator disagreement
4¡£6 granularity of sense: groupings for wordnet
4¡£6¡£1 criteria for wordnet sense grouping
4¡£6¡£2 analysis of sense grouping
4¡£7 senseval-3
4¡£8 discussion
references

5 knowledge-based methods for wsd
rada mihalcea
5¡£1 introduction
5¡£2 lesk algorithm
5¡£2¡£1 variations of the lesk algorithm
simulated annealing
simplified lesk algorithm
augmented semantic spaces
summary
5¡£3 semantic similarity
5¡£3¡£1 measures of semantic similarity
5¡£3¡£2 using semantic similarity within a local context
5¡£3¡£3 using semantic similarity within a global context
5¡£4 selectional preferences
5¡£4¡£1 preliminaries: learning word-to-word relations
5¡£4¡£2 learning selectional preferences
5¡£4¡£3 using selectional preferences
5¡£5 heuristics for word sense disambiguation
5¡£5¡£1 most frequent sense
5¡£5¡£2 one sense per discourse
5¡£5¡£3 one sense per collocation
5¡£6 knowledge-based methods at senseval-2
5¡£7 conclusions
references

6 unsupervised corpus-based methods for wsd
ted pedersen
6¡£1 introduction
6¡£1¡£1 scope
6¡£1¡£2 motivation
distributional methods
translational equivalence
6¡£1¡£3 approaches
6¡£2 type-based discrimination
6¡£2¡£1 representation of context
6¡£2¡£2 algorithms
latent semantic analysis £¨lsa£©
hyperspace analogue to language £¨hal£©
clustering by committee £¨cbc£©
6¡£2¡£3 discussion
6¡£3 token-based discrimination
6¡£3¡£1 representation of context
6¡£3¡£2 algorithms
context group discrimination
mcquitty's similarity analysis
6¡£3¡£3 discussion
6¡£4 translational equivalence
6¡£4¡£1 representation of context
6¡£4¡£2 algorithms
6¡£4¡£3 discussion
6¡£5 conclusions and the way forward
acknowledgments
references

7 supervised corpus-based methods for wsd
8 knowledge sources for wsd
9 automatic acquisition of lexical information and examples
10 domain-specific wsd
11 wsd in nlp applications