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

Autor:	Georgiev, Georgi, Nakov, Preslav, Ganchev, Kuzman, Osenova, Petya, Simov, Kiril Ivanov
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning 68T50 F.2.2 I.2.7
Zdroj:	RANLP-2009
Druh dokumentu:	Working Paper
Popis:	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. Comment: named entity recognition, NER, conditional random fields, CRF, Bulgarian, BulTreeBank
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.15121 Zobrazit plný text záznamu View this record from Arxiv