Using Wikipedia for Cross-Language Named Entity Recognition

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

Named entity recognition and classification (NERC) is fundamental for natural language processing tasks such as information extraction, question answering, and topic detection. State-of-the-art NERC systems are based on supervised machine learning and hence need to be trained on (manually) annotated corpora. However, annotated corpora hardly exist for non-standard languages and labeling additional data manually is tedious and costly. In this article, we present a novel method to automatically generate (partially) annotated corpora for NERC by exploiting the link structure of Wikipedia. Firstly, Wikipedia entries in the source language are labeled with the NERC tag set. Secondly, Wikipedia language links are exploited to propagate the annotations in the target language. Finally, mentions of the labeled entities in the target language are annotated with the respective tags. The procedure results in a partially annotated corpus that is likely to contain unannotated entities. To learn from such partially annotated data, we devise two simple extensions of hidden Markov models and structural perceptrons. Empirically, we observe that using the automatically generated data leads to more accurate prediction models than off-the-shelf NERC methods. We demonstrate that the novel extensions of HMMs and perceptrons effectively exploit the partially annotated data and outperforms their baseline counterparts in all settings.

Original languageEnglish
Title of host publicationBig Data Analytics in the Social and Ubiquitous Context : 5th International Workshop on Modeling Social Media, MSM 2014, 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, Revised Selected Papers
EditorsMartin Atzmüller, Alvin Chin, Frederik Janssen, Immanuel Schweizer, Christoph Trattner
Number of pages25
PublisherSpringer International Publishing AG
Publication date2016
Pages1-25
ISBN (Print)978-3-319-29008-9
ISBN (Electronic)978-3-319-29009-6
DOIs
Publication statusPublished - 2016
Event 5th International Workshop on Mining Ubiquitous and Social Environments - MUSE 2014 - Nancy, France
Duration: 15.09.201415.09.2014
Conference number: 5
https://www.semanticscholar.org/paper/The-Fifth-International-Workshop-on-Mining-and-Qin-Greene/03ed707786c842ce7a36b091457e1452d2723aec
https://www.kde.cs.uni-kassel.de/wp-content/uploads/ws/muse2014/

    Research areas

  • Business informatics - Hide Markov Model, Target Language, Conditional Random Field, Source Language, Entitiy Recognition