Feature-rich named entity recognition for bulgarian using conditional random fields

Georgi Georgiev, Preslav Nakov, Kuzman Ganchev, Petya Osenova, Kiril Simov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian. We combine well-established features used for other languages with language-specific lexical, syntactic and morphological information. In particular, we make use of the rich tagset annotation of the BulTreeBank (680 morpho-syntactic tags), from which we derive suitable task-specific tagsets (local and nonlocal). We further add domain-specific gazetteers and additional unlabeled data, achieving F 1=89.4%, which is comparable to the state-of-the-art results for English.

Original languageEnglish
Title of host publicationInternational Conference Recent Advances in Natural Language Processing, RANLP
Pages113-117
Number of pages5
Publication statusPublished - 2009
Externally publishedYes
EventInternational Conference on Recent Advances in Natural Language Processing, RANLP-2009 - Borovets, Bulgaria
Duration: 14 Sep 200916 Sep 2009

Other

OtherInternational Conference on Recent Advances in Natural Language Processing, RANLP-2009
CountryBulgaria
CityBorovets
Period14/9/0916/9/09

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Keywords

  • Conditional random fields
  • Information extraction
  • Linear models
  • Machine learning
  • Morphology
  • Named entity recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

Cite this

Georgiev, G., Nakov, P., Ganchev, K., Osenova, P., & Simov, K. (2009). Feature-rich named entity recognition for bulgarian using conditional random fields. In International Conference Recent Advances in Natural Language Processing, RANLP (pp. 113-117)