ABSTRACT
In this paper we describe the mapping of Zaliznjak's (1977) morphological classes into the lexical representation language DATR (Evans and Gazdar 1996). On the basis of the resulting DATR theory a set of fully inflected forms together with their associated morphosyntax can automatically be generated from the electronic version of Zaliznjak's dictionary (Ilola and Mustajoki 1989). From this data we plan to develop a wide-coverage morphosyntactic lemma-tizer and tagger for Russian.
- Anciaux, Michele. 1991. Word-form Recognition and Generation: A Computational Approach to Russian Morphology. PhD dissertation, University of Washington.Google Scholar
- Brown, Dunstan, Greville Corbett and Norman Fraser. 1995. rusnoms.dtr - a fragment for the nominal system of Russian. Available from the DATR archive http://www.datr.orgGoogle Scholar
- Brown, Dunstan, Andrew Hippisley, Greville Corbett and Norman Fraser. 1995. rusnlex.dtr - lexicon of frequent Russian noun. Available from the DATR archive http://www.datr.orgGoogle Scholar
- Brown, Dunstan, Greville Corbett, Norman Fraser, Andrew Hippisley and Alan Timberlake. 1996. Russian noun stress and network morphology. Linguistics 34. 53--107.Google Scholar
- Brown, Dunstan. 1998. From the General to the Exceptional: A Network Morphology Account of Russian Nominal Inflection. PhD thesis, University of Surrey.Google Scholar
- Cahill, Lynne and Gerald Gazdar. 1999. The POLYLEX architecture: multilingual lexicons for related languages. Traitement Automatique des Languages, 40(2):5--23.Google Scholar
- Corbett, Greville G. and Norman M. Fraser. 1993. Network morphology: A DATR account of Russian nominal inflection. Journal of Linguistics 29. 113--42.Google Scholar
- Dimitrova, Ludmila, Tomaž Erjavec, Nancy Ide, Heiki Jaan Kaalep, Vladimir Petkevič, Dan Tufis. 1998. Multext-East: Parallel and Comparable Corpora and Lexicons for Six Central and Eastern European Languages. In Proceedings of COLING-ACL '98. 315--319. Google ScholarDigital Library
- Evans, Roger and Gerald Gazdar. 1996. DATR: A Language for Lexical Knowledge Representation. Computational Linguistics 22. 167--216. Google ScholarDigital Library
- Fraser, Norman M. and Greville G. Corbett. 1995. Gender, animacy and declensional class assignment: a unified account for Russian. In G. Booij and J. van Marle (eds.) Yearbook of Morphology 1994. Dordrecht: Kluwer. 123--150.Google Scholar
- Ilola, Eeva&Mustajoki, Arto. 1989. Report on Russian Morphology as it appears in Zaliznyak's Grammatical Dictionary. Helsinki: Helsinki University Press.Google Scholar
- Lönngren, Lennart (ed.) 1993. Častotnyj slovar' sovre-mennogo russkogo jazyka. Uppsala: Uppsala University. (=Studia Slavica Upsaliensia 32).Google Scholar
- Maier, I. 1994. Review of Lönngren (ed.) Častotnyj slovar' sovremennogo russkogo jazyka. Rusistika Segodnja 1. 130--136.Google Scholar
- Pavlova, E., Y. Pavlov, R. Sproat, C. Shih and J. van Santen. 1997. Bell Laboratories Russian Text-to-Speech System. In G. Kokkinakis, N. Fakotakis, E. Dermatas (eds.) Eurospeech '97 Proceedings. Volume 5. 2451--2454.Google Scholar
- Tiberius, Carole. 2001. Architectures for Multilingual Lexical Representation. PhD Thesis, ITRI, University of Brighton.Google Scholar
- Vitas, Dusko. 2001. Intex and Slavonic Morphology. In Proceedings of the 4th Intex workshop. Bordeaux. Available online at: http://grelis.univ-fcomte.fr/intex/downloads/DuskoVitas.pdfGoogle Scholar
- Zaliznjak, A. A. 1977. Grammatičeskij slovar' russkogo jazyka. Moscow: Russkij jazyk.Google Scholar
- Zasorina, L. N. 1977. Častotnyj slovar' russkogo jazyka. Moscow: Russkij jazyk.Google Scholar
Recommendations
Lexicon+TX: rapid construction of a multilingual lexicon with under-resourced languages
Most efforts at automatically creating multilingual lexicons require input lexical resources with rich content (e.g. semantic networks, domain codes, semantic categories) or large corpora. Such material is often unavailable and difficult to construct ...
A statistical tagger for morphological tagging of Russian language texts
We consider a method of constructing a statistical tagger for automated morphological tagging for Russian language texts. In this method, each word is assigned with a tag that contains information about the part of speech and a full set of the word's ...
Evaluation of Morphological Embeddings for the Russian Language
NLPIR '19: Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information RetrievalA number of morphology-based word embedding models were introduced in recent years. However, their evaluation was mostly limited to English, which is known to be a morphologically simple language. In this paper, we explore whether and to what extent ...
Comments