GenSense: A Generalized Sense Retrofitting Model
作者： Hsin-Hsi Chen , Yang-Yin Lee , Hen-Hsen Huang , Yow-Ting Shiue , Ting-Yu Yen
DOI:
关键词: Computer science 、 Word embedding 、 Word (computer architecture) 、 Similarity (psychology) 、 Representation (arts) 、 Relation (database) 、 Theoretical computer science 、 Semantic similarity 、 Embedding 、 Generalization
摘要: With the aid of recently proposed word embedding algorithms, study semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To address this issue, some researches propose learning algorithms. In paper, we present a generalized model from existing retrofitting model. The generalization takes three major components: relations between senses, relation strength strength. experiment, show that can outperform previous approaches in types experiment: relatedness, contextual difference.
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