Open AccessTagging Named Entities in Croatian Tweets
Author(s) -
Krešimir Baksa,
Dino Golović,
Goran Glavaš,
Jan Šnajder
Publication year - 2017
Publication title -
slovenščina 2 0 empirične aplikativne in interdisciplinarne raziskave
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.165
H-Index - 1
ISSN - 2335-2736
DOI - 10.4312/slo2.0.2016.1.20-41
Subject(s) - named entity recognition , sequence labeling , conditional random field , croatian , hidden markov model , computer science , natural language processing , task (project management) , artificial intelligence , set (abstract data type) , named entity , feature (linguistics) , f1 score , sequence (biology) , information retrieval , linguistics , engineering , philosophy , programming language , biology , genetics , systems engineering
Named entity extraction tools designed for recognizing named entities in texts written in standard language (e.g., news stories or legal texts) have been shown to be inadequate for user-generated textual content (e.g., tweets, forum posts). In this work, we propose a supervised approach to named entity recognition and classification for Croatian tweets. We compare two sequence labelling models: a hidden Markov model (HMM) and conditional random fields (CRF). Our experiments reveal that CRF is the best model for the task, achieving a very good performance of over 87% micro-averaged F1 score. We analyse the contributions of different feature groups and influence of the training set size on the performance of the CRF model
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom