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The assessment of word sense disambiguation systems is usually performed in terms of evaluation measures borrowed from the field of information retrieval, that we introduce hereafter

Word sense disambiguation: A survey

ACM Comput. Surv., no. 2 (2009): ArticleNo.10-ArticleNo.10

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word sense disambiguationsemeval campaignwsd systemknowledge-based approachword sense discrimination更多(13+)

摘要

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity o...更多

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简介

That many words can be interpreted in multiple ways depending on the context in which they occur.
Consider the following sentences:.
(a) the author can hear bass sounds.
(b) They like grilled bass.
The occurrences of the word bass in the two sentences clearly denote different meanings: low-frequency tones and a type of fish, respectively.
The identification of the specific meaning that a word assumes in context is only apparently simple.
While most of the time humans do not even think

重点内容

We present here the evaluation measures and baselines employed for in vitro evaluation of Word sense disambiguation systems, that is, as if they were stand-alone, independent applications
The assessment of word sense disambiguation systems is usually performed in terms of evaluation measures borrowed from the field of information retrieval, that we introduce hereafter
Most of the pre-Senseval results are not comparable with subsequent approaches in the field
The Senseval workshops are the best reference to study the recent trends of Word sense disambiguation and the future research directions in the field
The hardness of Word sense disambiguation strictly depends on the granularity of the sense distinctions taken into account

方法

Sometimes different classifiers are available which the authors want to combine to improve the overall disambiguation accuracy.
Combination strategies—called ensemble methods— typically put together learning algorithms of different nature, that is, with significantly different characteristics.
Features should be chosen so as to yield significantly different, possibly independent, views of the training data

结果

EVALUATION METHODOLOGY

The authors present here the evaluation measures and baselines employed for in vitro evaluation of WSD systems, that is, as if they were stand-alone, independent applications.
The assessment of word sense disambiguation systems is usually performed in terms of evaluation measures borrowed from the field of information retrieval, that the authors introduce hereafter.
The Senseval workshops are the best reference to study the recent trends of WSD and the future research directions in the field
They lead to the periodic release of data sets of high value for the research community

结论

In this article the authors surveyed the field of word sense disambiguation.
A broad account of the history and literature in the field can be found in Ide and Veronis [1998] and Agirre and Edmonds [2006].
The hardness of WSD strictly depends on the granularity of the sense distinctions taken into account.
The problem gets much harder when it comes to a more general notion of polysemy, where sense granularity makes the difference both in the performance of disambiguation systems and in the agreement between human annotators

表格

Table1: Sentence
Table2: WordNet Sense Inventory for the First Three Senses of keyn Definition and Examples
Table3: Different Sizes of Word Contexts Context Example
Table4: Topic Signatures for the Two Senses of waitern Topic Signature restaurant, waitress, dinner, bartender, dessert, dishwasher, aperitif, brasserie, . . . hospital, station, airport, boyfriend, girlfriend, sentimentalist, adjudicator,
Table5: Performance of the Highest-Ranking Systems Participating in the
Table6: Performance of Semantic Similarity Measures and a Lesk-Based Gloss Overlap Measure on 1754 Noun Instances from the Senseval-2 Lexical Sample Data Set (cf
Table7: An Example of Decision List
Table8: Performance of the Highest-Ranking Systems Participating in the Lexical Sample and

Download tables as Excel

基金

This work was partially funded by the Interop NoE (508011) 6th EU FP

引用论文

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Received December 2006; revised January 2008; accepted March 2008

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Roberto Navigli

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