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PhD positions in NLP Centre

The NLP Centre offers long term positions in research and developmnet projects for students of Masters or Doctoral programs who are planning post gradual studies. These positions are paid with scholarships or as part-time job and significantly increases the standard doctoral scholarship.

TextMiner project

Advisor:doc. Aleš Horák
Period: 2016-201
Provider: Ministry of the Interior of the Czech Republic
1-2 positions

The new three-year long project concentrates on entities and relations extraction from large texts. The aim of the project is to create tools for natural language processing which will allow to extract information about entities (name of person, place or organization, time, email or telephone number) and relations between these entities and mine information based on author's style (author's education and sex, origin of the text – if the text is a machine translation). All the characteristics of text mentioned above will be acquired by text analysis with use of tools of language analysis, machine learning techniques and stylometry.

OCR Miner project

Advisor: doc. Aleš HorákPeriod: 2016-2018
Provider: Konica Minolta
1 position

New cooperation with Faculty of Informatic's industrial partner Konica Minolta. The aim of the project is proposal and implementation of algorithms for information extraction from scanned documents.

DEB LDI procect

Advisor: doc. Aleš Horák, dr. Adam Rambousek
Period: 2016-2017
Provider: Ministry of Education of the Czech Republic
1 position

Part of the ​European Network of e-lexicography project. Developing of tools for editing and connecting dictionaries (Linded Data).


Advisor: doc. Pavel Rychlý
Period: 2016-2019
Provider: Lexical Computing
1 position

The DIACRAN II project follows the previous research project DIACRAN in which automatic methods for detecting trends on word layer in language development over time were developed. The aim of the project is to further develop these methods for the meaning of words defined in context by their use in real texts meaning automaticly detect systematic changes of words context indicating change (loss/gain) of the meaning.