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Korpus Języka Polskiego Politechniki Wrocławskiej (KPWr) 1.3
No full textKPWr (Polish Corpus of Wrocław University of Technology, pol. Korpus Języka Polskiego Politechniki Wrocławskiej) is a corpus of written and spoken documents available on the Creative Common license. The texts are divided into 15 subcorpuses (blogs, science, stenographic recordings, etc.). The documents are annotated on the level of chunks and selected predicate-argument relations, named entities, relations between named entities, anaphora relations, word senses, events, temporal expressions, spatial relations between entities, keywords and semantic roles within nominal and adjective phrase
PolEval 2019 Task 2: Lemmatization of proper names and multi-word phrases — train, tune and test data.
No full textThe task consists in developing a tool for the lemmatization of proper names and multi-word phrases. The generated lemmas should follow the KPWr guidelines [https://clarin-pl.eu/dspace/handle/11321/625].
— poleval2019_task2_train.zip contains training data,
— poleval2019_task2_tune.zip contains data used for the initial evaluation,
— poleval2019_task2_test.zip contains data used for the final evaluation
Assamese POS-Tagged Text
No full textAssamese POS tagger is a CRF++ based POS Tagger. Raw text is given to this CRF++ based POS tagger to get POS tagged data. Standard POS tagset is used.
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1. These Assamese NLP resources including the Tools and Applications are developed
during Research and Development Projects as well as Masters and Ph.D. thesis
works.
2. These are mainly developed or generated at Gauhati University Department of
Computer Science and Department of Information Technology.
3. These resources are used by students and researchers for further studies, researches, as
well as for design and development of tools and applications.
4. Computational Linguistics in Assamese is not rich, and Natural Language Processing
works have mainly started during last two decades, and most of the resources are first
generation resources, and with ample scope for upgrading, enriching, and purifying.
5. These are very good and essential resources for all the researchers in Assamese NLP, as
the language requires more and more NLP works to make Assamese a rich media for
the digital world.
6. Anyone interested, or in need of such resources may express their interest for the
required resources, and the way of availability will be advised/informed accordingly.
7. These are purely research materials and could only be used for further research only.
8. Researchers may visit the NLP Lab of Department of Information Technology, Gauhati
University, Guwahati, India or contact us.
9. Researchers interested in collaborative works, and also students for project works, are
welcome.
10. Contact person is Professor Shikhar Kr. Sarma, Department of Information
Technology, Gauhati University, Guwahati 781014, Assam, India. Email-
[email protected]
KPWr Annotation Guidelines - Spatial Expressions (2.0)
No full textAn enhanced version of spatial expressions annotation guidelines describing the process of manual annotation of documents in Polish Corpus of Wrocław University of Technology (KPWr)
The system of the diagnostics in plWordNet
No full textThe pdf-document contains the description of the most frequent, regular errors in plWordNet and rules of them semi-automatic correction
plWordNet 4.1 – a Linguistically Motivated, Corpus-based Bilingual Resource
Get PDFThe paper presents the latest release of the Polish WordNet, namely plWordNet 4.1. The most significant developments since 3.0 version include new relations for nouns and verbs, mapping semantic role-relations from the valency lexicon Walenty onto the plWordNet structure and sense-level interlingual mapping. Several statistics are presented in order to illustrate the development and contemporary state of the wordnet
KPWr dump r240
No full textDump of the Polish Corpus of Wrocław University of Technology (KPWr) containing a set of documents annotated with named entities and keywords
Testing Zipf’s meaning-frequency law with wordnets as sense inventories
Get PDFAccording to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, French, Polish, Japanese, Indonesian and Chinese. We have proved that the law works pretty well for all of these languages if we take - as Zipf did - mean values of meaning count and averaged ranks. On the other hand, the law disastrously fails in predicting the number of senses for a single lemma. We have also provided the evidence that slope coefficients of Zipfian log-log linear model may vary from language to language
Liner2 temporal expressions model
No full textLiner2 model for temporal expression recognition and normalisatio