基于分布式叙词表嵌入的上下义关系检测
Using Distributional Thesaurus Embedding for Co-hyponymy Detection
摘要：
区分分布相似词之间的词汇关系一直是自然语言处理（NLP）领域的一个挑战。本文研究了分布式叙词表的网络嵌入能否有效地用于共上下义关系的检测。通过对三个基准数据集的广泛实验，我们表明，通过在分布式词库上应用node 2 vec获得的向量表示优于最先进的二元分类模型，用于co-hyponymy与hyponymy，以及co-hyponymy与meronymy。
英文摘要：
Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community. In this paper, we investigate whether the network embedding of distributional thesaurus can be effectively utilized to detect co-hyponymy relations. By extensive experiments over three benchmark datasets, we show that the vector representation obtained by applying node2vec on distributional thesaurus outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-hyponymy vs. meronymy, by huge margins.
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。
