1. Neural Named Entity Boundary Detection
- Author
-
Jing Li, Yukun Ma, and Aixin Sun
- Subjects
Vocabulary ,business.industry ,Computer science ,media_common.quotation_subject ,Boundary (topology) ,Pattern recognition ,02 engineering and technology ,Computer Science Applications ,Named entity ,Entity linking ,Computational Theory and Mathematics ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,business ,Sentence ,Information Systems ,media_common - Abstract
In this paper, we focus on named entity boundary detection , which is to detect the start and end boundaries of an entity mention in text, without predicting its type. The detected entities are input to entity linking or fine-grained typing systems for semantic enrichment. We propose BdryBot , a recurrent neural network encoder-decoder framework with a pointer network to detect entity boundaries from a given sentence. The encoder considers both character-level representations and word-level embeddings to represent the input words. In this way, BdryBot does not require any hand-crafted features. Because of the pointer network, BdryBot overcomes the problem of variable size output vocabulary and the issue of sparse boundary tags. We conduct two sets of experiments, in-domain detection and cross-domain detection, on six datasets. Our results show that BdryBot achieves state-of-the-art performance against five baselines. In addition, our proposed approach can be further enhanced when incorporating contextualized language embeddings into token representations.
- Published
- 2021