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Developing free morphological data for Polish

100%
Radziszewski A., Maziarz M.
Cognitive Studies
|
2011
|
issue 11
EN
Developing free morphological data for PolishA limiting factor in construction of Natural Language Processing (NLP) systems is often the availability of morphological resources. This indeed happens for Polish: the freely available corpus with manual morpho-syntactic annotation (part of the IPI PAN Corpus) is not coupled with any free morphological analyser. There exists a very large morphological dictionary of Polish available under a free licence – Morfologik. Unfortunately, its tagset differs significantly from the tagset of the corpus and, what is more, its morphological description lacks desired rigour. We amend this situation by performing a massive conversion of the dictionary into the tagset compliant with the corpus. The conversion results in a free dictionary containing entries for almost 3.5 million different word forms. In this article we report on our methodology, discuss some morphological and syntactic issues related to both tagsets and present the characteristics of the resulting dictionary.
2

Shallow syntactic annotation in the corpus of Wrocław University of Technology

81%
Radziszewski A., Maziarz M., Wieczorek J.
Cognitive Studies
|
2012
|
issue 12
EN
Shallow syntactic annotation in the corpus of Wrocław University of TechnologyIn this paper we present shallow syntactic annotation of The Wrocław University of Technology Corpus. We discuss some theoretical and practical considerations related to shallow parsing of Polish, then we present our annotation guidelines. The proposed annotation scheme includes chunking – four chunk types are defined with reference to the notion of accommodation and syntactic connotation, as well as annotation of four inter-chunk predicate-argument relations. Until now almost 18k chunk and 4k relation instances have been annotated. We believe that both the corpus and the annotation guideliness will prove their applicability in construction of automatic shallow parsers.
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