Named Entity Recognition for Pharmaceutics Domain: Design and Implementation for the Russian Language

Student: Druzhkina Anna

Supervisor: Irina Efimenko

Educational Programme: Computational Linguistics (Master)

Final Grade: 9

Year of Graduation: 2016

The paper presents a project and realization of a hybrid named entity recognition system for the Russian language in the Pharmaceutics domain. The project is realized in Python. System architecture is mainly based on machine learning (Conditional Random Fields) accompanied by rules and use of lexical resources. The system was trained and tested on different parts of a corpus of patents, which was manually collected and annotated with 4 types of entities. The achieved results on this corpus are as follows: F-measure 77.79, precision 90.3, recall 73.3. The paper also provides discussion of the results, conclusions and perspectives of future work.

Full text (added June 6, 2016)

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