Version 6 (modified by Ales Horak, 7 years ago) (diff)


PhD positions in NLP Centre

The NLP Centre offers longer term (1-3 years) positions in research and development projects for students of Doctoral programs or Master programs who are planning post gradual studies. These positions are financed via scholarships or as part-time jobs and can significantly increase the standard doctoral scholarship at FI MU.

TextMiner project

Advisor:doc. Aleš Horák
Period: 2016-2019
Provider: Ministry of the Interior of the Czech Republic
1-2 positions

The new three-years long project concentrates on named entity and relation 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 (personal name, location or organization, time, email or telephone number) and relations between these entities and to 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 text characteristics 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 Horák
Period: 2016-2018
Provider: Konica Minolta
1 position

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

DEB LDI project

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

Project connected with the ​European Network of e-lexicography. Development of tools for editing and connecting dictionaries (Linked Data).

DIACRAN II project

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 to derive the meaning of words defined in by the context of their use in real texts meaning and automatically detect systematic changes of words contexts that indicate a change (loss/gain) of the word meaning.