论文标题
使用分布词库嵌入共同检测
Using Distributional Thesaurus Embedding for Co-hyponymy Detection
论文作者
论文摘要
在分布相似的词之间区分词汇关系一直是自然语言处理（NLP）社区的挑战。在本文中，我们研究了是否可以有效地利用分布词库的网络嵌入来检测共同象征关系。通过三个基准数据集的广泛实验，我们表明，通过在分布词库上应用Node2VEC获得的矢量表示胜过二元分类的共同象征性与超级nymymy的二元分类模型，以及高级nompynymy以及Co-Hyponymy vs. Meronymy，以及巨大的余额。
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.
