The meaning of a word includes a conceptual meaning and a distributive meaning.Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity,especially for l...The meaning of a word includes a conceptual meaning and a distributive meaning.Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity,especially for low-frequency words.In knowledge bases,manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted.In this paper,we propose a Conceptual Semantics Enhanced Word Representation(CEWR)model,computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus,and aggregating it with distributed word representation to have both distributed information and the conceptual meaning encoded in the representation of words.We evaluate the CEWR model on two tasks:word similarity computation and short text classification.The Spearman correlation between model results and human judgement are improved to 64.71%,81.84%,and 85.16%on Wordsim297,MC30,and RG65,respectively.Moreover,CEWR improves the F1 score by 3%in the short text classification task.The experimental results show that CEWR can represent words in a more informative approach than distributed word embedding.This proves that conceptual semantics,especially hypernymous information,is a good complement to distributed word representation.展开更多
基金This research is supported by the National Science Foundation of China(grant 61772278,author:Qu,W.grant number:61472191,author:Zhou,J.http://www.nsfc.gov.cn/)+2 种基金the National Social Science Foundation of China(grant number:18BYY127,author:Li B.http://www.cssn.cn)the Philosophy and Social Science Foundation of Jiangsu Higher Institution(grant number:2019SJA0220,author:Wei,T.https://jyt.jiangsu.gov.cn)Jiangsu Higher Institutions’Excellent Innovative Team for Philosophy and Social Science(grant number:2017STD006,author:Gu,W.https://jyt.jiangsu.gov.cn)。
文摘The meaning of a word includes a conceptual meaning and a distributive meaning.Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity,especially for low-frequency words.In knowledge bases,manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted.In this paper,we propose a Conceptual Semantics Enhanced Word Representation(CEWR)model,computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus,and aggregating it with distributed word representation to have both distributed information and the conceptual meaning encoded in the representation of words.We evaluate the CEWR model on two tasks:word similarity computation and short text classification.The Spearman correlation between model results and human judgement are improved to 64.71%,81.84%,and 85.16%on Wordsim297,MC30,and RG65,respectively.Moreover,CEWR improves the F1 score by 3%in the short text classification task.The experimental results show that CEWR can represent words in a more informative approach than distributed word embedding.This proves that conceptual semantics,especially hypernymous information,is a good complement to distributed word representation.
