Transfer learning for industrial applications of named entity recognition

Lingzhen Chen, Alessandro Moschitti, Giuseppe Castellucci, Andrea Favalli, Raniero Romagnoli

Research output: Contribution to journalConference article


In this paper, we propose a Transfer Learning technique for Named Entity Recognition that is able to flexibly deal with domain changes. The proposed technique is able to manage both the case when the set of named entities does not change and the case when the set of named entities changes in the target domain. In particular, we focus on the case when the target data contains only the annotation of a target named entity, and the source data is no longer available for the target task. Our solution consists in transferring the parameters from a source model, which are then fine-tuned with the target data. The model architecture is modified when recognizing a new category by adding properly new neurons to the model. Our experiments show that it is possible to effectively transfer learned parameters in both the scenarios, resulting in strong performances over the target categories without degrading the performances on the other named entities.

Original languageEnglish
Pages (from-to)129-140
Number of pages12
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2018
Event2nd Workshop on Natural Language for Artificial Intelligence, NL4AI 2018 - Trento, Italy
Duration: 22 Nov 201823 Nov 2018



  • Named entity recognition
  • Recurrent neural network
  • Sequence labeling
  • Transfer learning

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Chen, L., Moschitti, A., Castellucci, G., Favalli, A., & Romagnoli, R. (2018). Transfer learning for industrial applications of named entity recognition. CEUR Workshop Proceedings, 2244, 129-140.