Paper Info
Title
HELD: Hierarchical entity-label disambiguation in named entity recognition task using deep learning
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.
Year
DOI
Venue
2022
10.3233/IDA-205720
INTELLIGENT DATA ANALYSIS
Keywords
DocType
Volume
Fine-grained entity labels, hierarchical entity-label disambiguation using context, named entity recognition, deep learning, police reports domain
Journal
26
Issue
ISSN
Citations 
3
1088-467X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Barbara Stephanie Neves Oliveira100.34
Andreza Fernandes de Oliveira200.34
Vinicius Monteiro de Lira300.34
ticiana43214.96
Jose Antonio Fernandes de Macedo500.34