PhD student position in topic Natural language processing

Updated: 28 days ago
Job Type: FullTime
Deadline: 20 May 2021

KInIT realizes PhD study in partnership with Faculty of Information Technology, Brno University of Technology.

KInIT doctoral students will be full-time KInIT employees and devote their time to research and study for their PhD degree. At the same time, KInIT doctoral students will act as external students of FIT VUT and graduates will receive their degree from FIT VUT.

Supervising team: Marián Šimko (supervisor, KInIT), Jana Kosecka (George Mason University)

Key words: deep learning, multilingual learning, transparency, interpretability, information extraction, text classification

Continuous increase of data available world-wide emphasizes the need of its automatic processing and understanding. Particular challenges are posed by the heterogeneous and unstructured nature of text content provided in natural language. Natural language processing (NLP) ranks among the most prospective subfields of artificial intelligence with great potential of innovative applications affecting everyday life.

Recent advances in neural networks and machine learning allowed to push efficiency and scope of natural language understanding and generation forward. Yet, there remain many research challenges related to particular subtasks, application domains and languages. Further research and various resulting phenomena exploration is necessary. Special attention is drawn by the issues of interpretability and transparency of NLP models or by novel paradigms of learning addressing the problem of low-resource languages.

Particularly interesting challenges include, but are not limited to:

  • Transfer/multilingual learning
  • Interpretability and transparency for NLP
  • Domain-specific information extraction, text classification
  • Low-resource language processing
  • Visual grounding of natural language, image captioning

Relevant publications:

  • M. Pikuliak, M. Simko, M. Bielikova. Cross-lingual learning for text processing: A survey . Expert Systems with Applications, Vol. 165, March, 2021.
  • P. Korenek, M. Simko. Sentiment analysis on microblog utilizing appraisal theory . World Wide Web, 17,847–867, 2014.

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