A Named Entity Labeler for German: Exploiting Wikipedia and Distributional Clusters

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A Named Entity Labeler for German: Exploiting Wikipedia and Distributional Clusters
Authors
Grzegorz Chrupała
Dietrich Klakow
Publication date
2010
Links
Original

A Named Entity Labeler for German: Exploiting Wikipedia and Distributional Clusters - scientific work related to Wikipedia quality published in 2010, written by Grzegorz Chrupała and Dietrich Klakow.

Overview

Named Entity Recognition is a relatively well-understood NLP task, with many publicly available training resources and software for English. Other languages tend to be underserved in this area. For German, CoNLL-2003 provides training data, but there are no publicly available, ready-to-use tools. Authors fill this gap and develop a German NER system with state-of-the-art performance. In addition to CoNLL 2003 labeled training data, authors use two additional resources: (i) 32 million words of unlabeled text and (ii) infobox labels in German Wikipedia articles. Authors extract informative features of word-types from those resources and train a supervised model on the labeled training data. This approach allows us to deal better with word-types unseen in the training data and achieve state-of-the-art performance on German with little engineering effort.

Embed

Wikipedia Quality

Chrupała, Grzegorz; Klakow, Dietrich. (2010). "[[A Named Entity Labeler for German: Exploiting Wikipedia and Distributional Clusters]]".

English Wikipedia

{{cite journal |last1=Chrupała |first1=Grzegorz |last2=Klakow |first2=Dietrich |title=A Named Entity Labeler for German: Exploiting Wikipedia and Distributional Clusters |date=2010 |url=https://wikipediaquality.com/wiki/A_Named_Entity_Labeler_for_German:_Exploiting_Wikipedia_and_Distributional_Clusters}}

HTML

Chrupała, Grzegorz; Klakow, Dietrich. (2010). &quot;<a href="https://wikipediaquality.com/wiki/A_Named_Entity_Labeler_for_German:_Exploiting_Wikipedia_and_Distributional_Clusters">A Named Entity Labeler for German: Exploiting Wikipedia and Distributional Clusters</a>&quot;.