Topic Classification Problem Solving for Morphologically Complex Languages
作者： Jurgita Kapočiūtė-Dzikienė , Tomas Krilavičius
DOI: 10.1007/978-3-319-46254-7_41
关键词:
摘要: In this paper we are presenting a topic classification task for the morphologically complex Lithuanian and Russian languages, using popular supervised machine learning techniques. our research experimentally investigated two text methods big variety of feature types covering different levels abstraction: character, lexical, morpho-syntactic. order to have comparable results both kept experimental conditions as similar possible: datasets were composed normative texts, taken from news portals; contained topics; had same number texts in each topic.
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