TY - CHAP

T1 - Using Wikipedia for Cross-Language Named Entity Recognition

AU - Fernandes, Eraldo R.

AU - Brefeld, Ulf

AU - Blanco, Roi

AU - Atserias, Jordi

N1 - Conference code: 5

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Business informatics

KW - Hide Markov Model

KW - Target Language

KW - Conditional Random Field

KW - Source Language

KW - Entitiy Recognition

UR - http://www.scopus.com/inward/record.url?scp=84955265040&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-29009-6_1

DO - 10.1007/978-3-319-29009-6_1

M3 - Article in conference proceedings

SN - 978-3-319-29008-9

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 25

BT - Big Data Analytics in the Social and Ubiquitous Context

A2 - Atzmüller, Martin

A2 - Chin, Alvin

A2 - Janssen, Frederik

A2 - Schweizer, Immanuel

A2 - Trattner, Christoph

PB - Springer International Publishing AG

T2 - 5th International Workshop on Mining Ubiquitous and Social Environments - MUSE 2014

Y2 - 15 September 2014 through 15 September 2014

ER -