LearningToAdapt with word embeddings: Domain adaptation of Named Entity Recognition systems. Issue 3 (May 2021)

• Record Type: Journal Article
• Authors: Nozza, Debora; Manchanda, Pikakshi; Fersini, Elisabetta; Palmonari, Matteo; Messina, Enza
• Is Part Of: Information processing & management. Volume 58:Issue 3(2021)
• Publication Date: 2021-05
• Subjects: 68T50; Named Entity Recognition -- Domain adaptation -- Word embeddings; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102537
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: The task of Named Entity Recognition (NER) is aimed at identifying named entities in a given text and classifying them into pre-defined domain entity types such as persons, organizations, locations. Most of the existing NER systems make use of generic entity type classification schemas, however, the comparison and integration of (more or less) different entity types among different NER systems is a complex problem even for human experts. In this paper, we propose a supervised approach called L2AWE (Learning To Adapt with Word Embeddings) which aims at adapting a NER system trained on a source classification schema to a given target one. In particular, we validate the hypothesis that the embedding representation of named entities can improve the semantic meaning of the feature space used to perform the adaptation from a source to a target domain. The results obtained on benchmark datasets of informal text show that L2AWE not only outperforms several state of the art models, but it is also able to tackle errors and uncertainties given by NER systems. Highlights: Most of existing Named Entity Recognition systems make use of generic ontologies. The proposed model exploits Word Embeddings for improving the semantical meaning. The proposed model strongly improved the adaptation performance.