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Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields

2021-09-26 12:00:06
Georgi Georgiev, Preslav Nakov, Kuzman Ganchev, Petya Osenova, Kiril Ivanov Simov
arXiv_AI
     
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 F1=89.4%, which is comparable to the state-of-the-art results for English.

Abstract (translated)

URL

https://arxiv.org/abs/2109.15121

PDF

https://arxiv.org/pdf/2109.15121.pdf

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