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

Autor:	Georgiev, G., Preslav Nakov, Ganchev, K., Osenova, P., Simov, K.
Rok vydání:	2021
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Computation and Language Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence I.2.7 68T50 F.2.2 Computation and Language (cs.CL) Machine Learning (cs.LG)
Zdroj:	Scopus-Elsevier
DOI:	10.48550/arxiv.2109.15121
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:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bc794c8a5bf67288f75b282b509078a Zobrazit plný text záznamu