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KPWr annotation guidelines - named entity and phrase lemmatization 2.0
No full textGuidelines for named entity and multi-word phrase lemmatization used in in KPWr (Polish Corpus of Wrocław University of Technology)
Assamese spell variation list
No full textA spelling variant of a word occurs when a word may not have only a single correct spelling. There are many different ways in which it can be spelled in linguistics. A spell variation list comprising 5000 words, mainly named entities was compiled for Assamese language.
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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]
Assamese Stopwords
No full textThe most frequently occurring words in a context are the stopwords. They do not play an important role in retrieving information. As Stopwords do not contribute any important information towards the context and so they should be removed before processing. These words have very low discrimination value and are sometimes referred to as noise words. Assamese stopword list is created which contains 264 words. Examples are: যেতিয়া, যেন, যেনিবা, যেনে, যোগে, লগ, লৈ etc.
---
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]
Message board posts (pilot)
No full textCorpus of texts from message boards used to testing annotation of local grammar
Corpus of the Colloquial Polish Language
No full textThe Corpus of the Colloquial Polish Language (CCPL) is a UGC-based corpus tagged with morpho-syntactic features by the team of professional linguists from the Wrocław University of Technology. It consists of 400 000 tagged segments and has been used for training of the UGC-tagger, also available in the CLARIN repository.
Main resources:
Corpus files (NCP tagset): CCPL - anonimizacja_xml_out_ver(3.05).zip
Manual annotation guidelines: Specification for morphosyntactic tagging of UGC texts.pdf
Corpus files (UD tagset): corpus_petrov_tags.zi
1000PLUS Novels Corpus (1.0)
No full textCorpus of literary texts intended as benchmark collection for text categorization. It contains 1000 novels written in polish or translated to polish by various authors. This is an extension of 1000 Novels Corpus (http://hdl.handle.net/11321/312). Each text is stored as separate .txt file and .cmdi metadata description
Wroclaw Corpus of Consumer Reviews Sentiment (WCCRS)
No full textWroclaw Corpus of Consumer Reviews is a corpus of Polish reviews annotated with sentiment at the level of the whole text (*text*) and at the level of sentences (*sentence*) for the following domains: hotels, medicine, products and university (reviews*). Sentences are annotated with sentiment only for hotels and medicine. Each *sentence* file contains a single sentence with a sentiment __label__z_X and each *text* file contains a single review with a sentiment __label__meta_X. Regardless a resource type, X can be: minus_m -- strong negative; minus_s -- weak negative, zero -- neutral, amb -- ambiguous, plus_s -- weak positive, plus_m -- strong positive. *all* sets are groups of all domains within each text/sentence type. Train/dev/test divisions were used for the evaluation. Results are available in the following paper:
@InProceedings{Kocon2019,
Title = {{Multi-level analysis and recognition of the text sentiment on the example of consumer opinions}},
Author = {Koco{\'n}, Jan and Zaśko-Zielińska, Monika and Miłkowski, Piotr},
Booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2019},
Year = {2019},
}
Please cite this paper if you use this resource