Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41953
Title: Feature-rich named entity recognition for bulgarian using conditional random fields
Authors: Georgiev, G.
Nakov, P. 
Ganchev, K.
Osenova, P.
Simov, K.
Keywords: Conditional random fields
Information extraction
Linear models
Machine learning
Morphology
Named entity recognition
Issue Date: 2009
Source: Georgiev, G.,Nakov, P.,Ganchev, K.,Osenova, P.,Simov, K. (2009). Feature-rich named entity recognition for bulgarian using conditional random fields. International Conference Recent Advances in Natural Language Processing, RANLP : 113-117. ScholarBank@NUS Repository.
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.
Source Title: International Conference Recent Advances in Natural Language Processing, RANLP
URI: http://scholarbank.nus.edu.sg/handle/10635/41953
ISSN: 13138502
Appears in Collections:Staff Publications

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