融合多特征的专利功效短语识别

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Abstract Patent efficacy is one of the key information in the patent text. To identify the patent efficacy phrase, a multiple feature approach is proposed to combine both character-level features and word-level features. The character-level features include characters, character pinyin, and character wubi. The word-level features correspond to a collection of words containing those characters. Character-level features are vectorized by word2vec or BERT. Attention mechanism is used to fuse the word-level feature vectors in the input sequence. All feature vectors are concatenated as the input of BiLSTM (or Transformer)+CRF. Experiments on patents of new energy vehicles demonstrate the best 91.15% F1 value is achieved by BiLSTM+CRF with the combination of word2vec character vector, Bert character vector, wubi feature vector and word feature vector.

Key words： patent efficacy phrase multi-scale features fusion neural network attention mechanism

Received: 04 December 2020

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	Articles by authors
	LUO Yixiong
	LYU Xueqiang
	YOU Xindong

Cite this article:

LUO Yixiong,LYU Xueqiang,YOU Xindong. Patent Efficacy Phrase Recognition Based on Multiple Features[J]. , 2022, 36(12): 139-148.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I12/139

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