Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations
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DOI：
10.18653/v1/n18-1102
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Koki Washio;Tsuneaki Kato
中科院分区：
文献类型：
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作者：
Koki Washio;Tsuneaki Kato
识别词对之间的词汇语义关系是自然语言处理中的一个重要任务。其中一个主流的方法是利用连接两个目标词的词汇-句法路径，这些路径反映了词对之间的语义关系。然而，这种方法要求所考虑的单词在句子中共同出现。这一要求很难满足，因为齐普夫定律指出，大多数实义词很少出现。在本文中，我们提出了新的方法与神经模型的P（路径|w1，w2）来解决这个问题。我们提出的P（path）模型|w1，w2）可以以无监督的方式学习，并且可以概括词对和依存路径的共现。该模型可以用来扩充语料库中不共现的词对的路径数据，并从词对中提取捕捉关系信息的特征。我们的实验结果表明，我们的方法改进了以前的神经方法的依赖路径，并成功地解决了集中的问题。
Recognizing lexical semantic relations between word pairs is an important task for many applications of natural language processing. One of the mainstream approaches to this task is to exploit the lexico-syntactic paths connecting two target words, which reflect the semantic relations of word pairs. However, this method requires that the considered words co-occur in a sentence. This requirement is hardly satisfied because of Zipf’s law, which states that most content words occur very rarely. In this paper, we propose novel methods with a neural model of P(path|w1,w2) to solve this problem. Our proposed model of P (path|w1, w2 ) can be learned in an unsupervised manner and can generalize the co-occurrences of word pairs and dependency paths. This model can be used to augment the path data of word pairs that do not co-occur in the corpus, and extract features capturing relational information from word pairs. Our experimental results demonstrate that our methods improve on previous neural approaches based on dependency paths and successfully solve the focused problem.