A novel feature integration method for named entity recognition model in product titles

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shiqi Sun, Kun Zhang, Jingyuan Li, Xinghang Sun, Jianhe Cen, Yuanzhuo Wang
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引用次数: 0

Abstract

Entity recognition of product titles is essential for retrieving and recommending product information. Due to the irregularity of product title text, such as informal sentence structure, a large number of professional attribute words, a large number of unrelated independent entities of various combinations, the existing general named entity recognition model is limited in the e-commerce field of product title entity recognition. Most of the current studies focus on only one of the two challenges instead of considering the two challenges together. Our approach proposes NEZHA-CNN-GlobalPointer architecture with the addition of label semantic network, and uses multigranularity contextual and label semantic information to fully capture the internal structure and category information of words and texts to improve the entity recognition accuracy. Through a series of experiments, we proved the efficiency of our approach over a dataset of Chinese product titles from JD.com, improving the F1-value by 5.98%, when compared to the BERT-LSTM-CRF model on the product title corpus.

产品标题中命名实体识别模型的新型特征整合方法
产品标题的实体识别对于检索和推荐产品信息至关重要。由于产品标题文本的不规则性,如不正规的句子结构、大量的专业属性词、大量不相关的独立实体的各种组合等,现有的通用命名实体识别模型在电子商务领域的产品标题实体识别中受到了限制。目前的研究大多只关注这两个挑战中的一个,而没有将这两个挑战放在一起考虑。我们的方法提出了NEZHA-CNN-GlobalPointer架构,并加入了标签语义网络,利用多粒度上下文和标签语义信息,充分捕捉词和文本的内部结构和类别信息,提高实体识别准确率。通过一系列实验,我们证明了我们的方法在 JD.com 中文产品标题数据集上的效率,与产品标题语料库上的 BERT-LSTM-CRF 模型相比,F1 值提高了 5.98%。
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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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