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Natural Language Processing

Main research interest is computational lexical semantics, including word sense embeddings, word sense induction, extraction of lexical resources, and other related topics. Research group members are interested in argument mining, neural and statistical natural language processing, information retrieval, knowledge bases, machine learning and intersections/interactions of these fields.


We have been working on various topics such as Semantics, Dialogue Systems, Argument Retrieval, and Active learning for NLP. During the next five years, we are going to expand our research activities and both national and international outreach in the field of language technologies and deep learning for NLP.

Lexical semantics (word sense induction and disambiguation, frame induction and disambiguation, semantic similarity and relatedness, sense embeddings, automated construction and completion of lexical resources such as WordNet and FrameNet)
Argument mining (comparative argument mining, and argument retrieval)
Learning representations of linguistic symbolic structures (graphs) such as knowledge bases and lexical resource
NLP for a better society: recognition of fake news, hate speech, and related phenomena
Textual style transfer
Tensors for NLP: learning representations based on tensor decompositions and tensor networks