Journal
IEEE INTELLIGENT SYSTEMS
Volume 35, Issue 1, Pages 74-81Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MIS.2019.2952334
Keywords
Task analysis; Semantics; Feature extraction; Artificial neural networks; Immune system; Labeling; Human computer interaction
Funding
- National Natural Science Foundation of China [U1836205, 91746116]
- Science and Technology Projects of Guizhou Province [[2017]3002, [2018]1035]
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The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.
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