HELD: Hierarchical entity-label disambiguation in named entity recognition task using deep learning

Neves Oliveira B. S., Fernandes De Oliveira A., Monteiro De Lira V., Linhares Coelho Da Silva T., Fernandes De Macedo J. A.

Fine-grained entity labels  Hierarchical entity-label disambiguation using context  Artificial Intelligence  Theoretical Computer Science  Named entity recognition  Computer Vision and Pattern Recognition  Police reports domain  Deep Learning 

Named Entity Recognition (NER) is a challenging learning task of identifying and classifying entity mentions in texts into predefined categories. In recent years, deep learning (DL) methods empowered by distributed representations, such as word- and character-level embeddings, have been employed in NER systems. However, for information extraction in Police narrative reports, the performance of a DL-based NER approach is limited due to the presence of fine-grained ambiguous entities. For example, given the narrative report 'Anna stole Ada's car', imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.

Source: Intelligent data analysis 26 (2022): 637–657. doi:10.3233/IDA-205720

Publisher: Elsevier Science, Inc.,, New York, NY , Stati Uniti d'America

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