Year: 2018
Type: conference-paper
Abstract: 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.
Authors: Lingzhen Chen, Alessandro Moschitti, Giuseppe Castellucci, Andrea Favalli, Raniero Romagnoli
Institution: University of Trento
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2902824568