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Adapting a constituency parser to user-generated content in polish opinion mining

Pluwak A., Korczynski W., Kisiel-Dorohinicki M.

Computer Science

2016

Vol. 17 (1)

23--44

The paper focuses on the adjustment of NLP tools for Polish; e.g., morphological analyzers and parsers, to user-generated content (UGC). The authors discuss two rule-based techniques applied to improve their efficiency: pre-processing (text normalization) and parser adaptation (modified segmentation and parsing rules). A new solution to handle OOVs based on inflectional translation is also offered.

Linguistic aspects of text normalization in a polish text-to-speech system

Graliński F., Jassem K., Wagner A., Wypych M.

Systems Science

2006

Vol. 32, no 4

7-15

The paper addresses linguistic problems of text normalization for the Polish language. Text normalization, which converts the written form of a text into the spoken form, is one of the preprocessing steps in text-to-speech systems. Normalization of texts in analytic languages like English does not necessarily require deep linguistic analysis. However, it is shown here that for synthetic languages, like Polish, linguistic analysis is crucial for the normalization process. Existing Polish text-to-speech systems, even though highly estimated for the naturalness of output, do not solve main normalization problems. The authors’ team aims at developing a text-to-speech system that will include a strong text normalization module. The idea is to design the module using linguistic resources and mechanisms developed for a Machine Translation system, Translatica. Progress of research may be followed at www.poleng.pl, where the user may input a source Polish text in the written form and obtain its "translation" after normalization.