Exploration of noise strategies in semi-supervised named entity classification

Pooja Lakshmi Narayan, Ajay Nagesh, Mihai Surdeanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Noise is inherent in real world datasets and modeling noise is critical during training as it is effective in regularization. Recently, novel semi-supervised deep learning techniques have demonstrated tremendous potential when learning with very limited labeled training data in image processing tasks. A critical aspect of these semi-supervised learning techniques is augmenting the input or the network with noise to be able to learn robust models. While modeling noise is relatively straightforward in continuous domains such as image classification, it is not immediately apparent how noise can be modeled in discrete domains such as language. Our work aims to address this gap by exploring different noise strategies for the semi-supervised named entity classification task, including statistical methods such as adding Gaussian noise to input embeddings, and linguistically-inspired ones such as dropping words and replacing words with their synonyms. We compare their performance on two benchmark datasets (OntoNotes and CoNLL) for named entity classification. Our results indicate that noise strategies that are linguistically informed perform at least as well as statistical approaches, while being simpler and requiring minimal tuning.

Original languageEnglish (US)
Title of host publication*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics
PublisherAssociation for Computational Linguistics (ACL)
Pages186-191
Number of pages6
ISBN (Electronic)9781948087933
StatePublished - 2019
Event8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019 - Minneapolis, United States
Duration: Jun 6 2019Jun 7 2019

Publication series

Name*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics

Conference

Conference8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019
Country/TerritoryUnited States
CityMinneapolis
Period6/6/196/7/19

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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