Improving Language-Dependent Named Entity Detection - International Cross Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2017)
Conference Papers Year : 2017

Improving Language-Dependent Named Entity Detection

Gerald Petz
  • Function : Author
  • PersonId : 1026070
Dietmar Nedbal
  • Function : Author
  • PersonId : 1026072

Abstract

Named Entity Recognition (NER) and Named Entity Linking (NEL) are two research areas that have shown big advancements in recent years. The majority of this research is based on the English language. Hence, some of these improvements are language-dependent and do not necessarily lead to better results when applied to other languages. Therefore, this paper discusses TOMO, an approach to language-aware named entity detection and evaluates it for the German language. This also required the development of a German gold standard dataset, which was based on the English dataset used by the OKE 2016 challenge. An evaluation of the named entity detection task using the web-based platform GERBIL was undertaken and results show that our approach produced higher F1 values than the other annotators did. This indicates that language-dependent features do improve the overall quality of the spotter.
Fichier principal
Vignette du fichier
456304_1_En_22_Chapter.pdf (535.23 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01677147 , version 1 (08-01-2018)

Licence

Identifiers

Cite

Gerald Petz, Werner Wetzlinger, Dietmar Nedbal. Improving Language-Dependent Named Entity Detection. 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2017, Reggio, Italy. pp.330-345, ⟨10.1007/978-3-319-66808-6_22⟩. ⟨hal-01677147⟩
87 View
126 Download

Altmetric

Share

More