Approches d'analyze distributionnelle pour amélibrary la désambiguïsation sémantique
Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique
摘要：
词义消歧（WSD）改进了许多自然语言处理（NLP）应用，例如信息检索、机器翻译或词汇简化。词义消歧是指在一个多义词汇单位中，结合语境，从不同的词义中确定一个词义的能力。最直接的方法是在目标词的词义候选和其上下文的词义候选之间使用语义接近度。这种方法很容易导致组合爆炸。在本文中，我们提出了两种方法的基础上的分布分析，使减少指数复杂性，而不会失去的一致性。我们提出了一个比较之间的选择分布的邻居和线性最近的邻居。得到的数字表明，选择分布邻居导致更好的结果。
英文摘要：
Word sense disambiguation (WSD) improves many Natural Language Processing (NLP) applications such as Information Retrieval, Machine Translation or Lexical Simplification. WSD is the ability of determining a word sense among different ones within a polysemic lexical unit taking into account the context. The most straightforward approach uses a semantic proximity measure between the word sense candidates of the target word and those of its context. Such a method very easily entails a combinatorial explosion. In this paper, we propose two methods based on distributional analysis which enable to reduce the exponential complexity without losing the coherence. We present a comparison between the selection of distributional neighbors and the linearly nearest neighbors. The figures obtained show that selecting distributional neighbors leads to better results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
