Affiliations: [a] Insight Data Science Lab, Federal University of Ceará, Ceará, Brazil | [b] Institute of Information Science and Technologies, National Research Council, Pisa, Italy
Correspondence:
[*]
Corresponding author: Bárbara Stéphanie Neves Oliveira, Insight Data Science Lab, Federal University of Ceará, Ceará, Brazil. E-mail: [email protected].
Abstract: 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.
Keywords: Fine-grained entity labels, hierarchical entity-label disambiguation using context, named entity recognition, deep learning, police reports domain
