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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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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
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