ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

Zhukova, Anastasia; Hamborg, Felix; Gipp, Bela

ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2021

Autor:innen

Zhukova, Anastasia

Hamborg, Felix

Gipp, Bela

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

ZHANG, Chengzhi, ed., Philipp MAYR, ed., Wei LU, ed., Yi ZHANG, ed.. Extraction and Evaluation of Knowledge Entities from Scientific Documents 2021 Proceedings of the 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2021) co-located with JCDL 2021. New York: ACM, 2021, pp. 5-14. CEUR Workshop Proceedings. eISSN 1613-0073

Zusammenfassung

Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its common and viable use in many use cases, NER is barely applicable in domains where general categories are suboptimal, such as engineering or medicine. To facilitate NER of domain-specific types, we propose ANEA, an automated (named) entity annotator to assist human annotators in creating domain-specific NER corpora for German text collections when given a set of domain-specific texts. In our evaluation, we find that ANEA automatically identifies terms that best represent the texts’ content, identifies groups of coherent terms, and extracts and assigns descriptive labels to these groups, i.e., annotates text datasets into the domain (named) entities.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

information extraction, low-resource languages, named entity recognition, domain-specific texts

Konferenz

2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021), 30. Sept. 2021 - 30. Sept. 2021, online

Zitieren

ISO 690

ZHUKOVA, Anastasia, Felix HAMBORG, Bela GIPP, 2021. ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts. 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021). online, 30. Sept. 2021 - 30. Sept. 2021. In: ZHANG, Chengzhi, ed., Philipp MAYR, ed., Wei LU, ed., Yi ZHANG, ed.. Extraction and Evaluation of Knowledge Entities from Scientific Documents 2021 Proceedings of the 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2021) co-located with JCDL 2021. New York: ACM, 2021, pp. 5-14. CEUR Workshop Proceedings. eISSN 1613-0073

BibTex

@inproceedings{Zhukova2021Autom-56902,
  year={2021},
  title={ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts},
  url={http://ceur-ws.org/Vol-3004/},
  publisher={ACM},
  address={New York},
  series={CEUR Workshop Proceedings},
  booktitle={Extraction and Evaluation of Knowledge Entities from Scientific Documents 2021  Proceedings of the 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2021) co-located with JCDL 2021},
  pages={5--14},
  editor={Zhang, Chengzhi and Mayr, Philipp and Lu, Wei and Zhang, Yi},
  author={Zhukova, Anastasia and Hamborg, Felix and Gipp, Bela}
}

RDF

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    <dcterms:abstract xml:lang="eng">Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its common and viable use in many use cases, NER is barely applicable in domains where general categories are suboptimal, such as engineering or medicine. To facilitate NER of domain-specific types, we propose ANEA, an automated (named) entity annotator to assist human annotators in creating domain-specific NER corpora for German text collections when given a set of domain-specific texts. In our evaluation, we find that ANEA automatically identifies terms that best represent the texts’ content, identifies groups of coherent terms, and extracts and assigns descriptive labels to these groups, i.e., annotates text datasets into the domain (named) entities.</dcterms:abstract>
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URL der Originalveröffentl.

http://ceur-ws.org/Vol-3004/

Prüfdatum der URL

2022-03-17

Universitätsbibliographie

Nein