An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert

Autor:	Bao, Wei, Che, Hongshu, Zhang, Jiandong
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years. Our paper mainly discusses a methodology to analyze the effect that context has on human perception of similar words, which is the third task of SemEval 2020. We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT). Our team will_go won the 1st place in Finnish language track of subtask1, the second place in English track of subtask1. Comment: ACL-SemEval 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.01006 Zobrazit plný text záznamu View this record from Arxiv