A domain short text naming entity recognition method integrated with domain knowledge

Abstract

Addressing the issue of relatively low recognition rates for named entities in domain-specific short texts under resource-constrained computational environments, a novel hybrid model combining a Dual BiLSTM_CRF architecture with a fully connected network is designed to identify named entities in these texts. The model leverages critical knowledge entities and their key relationships from a domain knowledge graph, which undergoes projection transformation, clustering, and global vector word embedding processing. Based on the calculation of word vector similarities, it identifies similar critical knowledge entities to those being recognized within the domain. These identified knowledge entities are then substituted into the original domain short text, generating an augmented version that is fed, along with the unmodified text, into the model for named entity recognition. This integration of domain knowledge into the recognition process of named entities in domain-specific short texts has shown promising results. Experimental outcomes demonstrate that this method outperforms existing comparable approaches in terms of its enhanced identification capabilities.

Keywords

• knowledge graph|knowledge entity|named entity recognition|bidirectional long short-term memory networks