Bidirectional Long Short-Term Memory Networks for Relation Classification 论文

摘要

Relation classification is an important se-mantic processing, which has achieved great attention in recent years. The main challenge is the fact that important infor-mation can appear at any position in the sentence. Therefore, we propose bidirec-tional long short-term memory networks (BLSTM) to model the sentence with complete, sequential information about all words. At the same time, we also use fea-tures derived from the lexical resources such as WordNet or NLP systems such as dependency parser and named entity rec-ognizers (NER). The experimental results on SemEval-2010 show that BLSTM-based method only with word embeddings as input features is sufficient to achieve state-of-the-art performance, and import-ing more features could further improve the performance. 1