Grammatical Agreement and Automatic Morphological Disambiguation of Inflectional Languages

Petkevič, Vladimír

doi:10.1007/3-540-44805-5_6

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The full tag is replaced by the POS tag (the first two fields); there are 60 POS tags. The TM3 8 Czech morphological processing was studied by Petkevič (2001), Hlavácová (2001) (who focuses on handling OOV words), and Mráková and Sedlacek (2003) (who use partial parsing to reduce the set of possible analyses), inter alia. feature templates are included twice: once for the full tag and once for a coarser tag (the first PDT field, for which there are 12 possible values).…”

Section: Factored Tags and Estimationmentioning

confidence: 99%

Context-based morphological disambiguation with random fields

Smith

Tromble

2005

Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05

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Finite-state approaches have been highly successful at describing the morphological processes of many languages. Such approaches have largely focused on modeling the phone-or character-level processes that generate candidate lexical types, rather than tokens in context. For the full analysis of words in context, disambiguation is also required (Hakkani-Tür et al., 2000;Hajič et al., 2001). In this paper, we apply a novel source-channel model to the problem of morphological disambiguation (segmentation into morphemes, lemmatization, and POS tagging) for concatenative, templatic, and inflectional languages. The channel model exploits an existing morphological dictionary, constraining each word's analysis to be linguistically valid. The source model is a factored, conditionally-estimated random field (Lafferty et al., 2001) that learns to disambiguate the full sentence by modeling local contexts. Compared with baseline state-of-the-art methods, our method achieves statistically significant error rate reductions on Korean, Arabic, and Czech, for various training set sizes and accuracy measures.

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Section: Factored Tags and Estimationmentioning

confidence: 99%

Context-based morphological disambiguation with random fields

Smith

Tromble

2005

Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05

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“…The motivations for our work come from: a possibility of encoding long distance dependencies in decision rules, applications of rule-based tagging for Czech, e.g. [12,13], an improvement introduced by the combined approach in [9] and an the work on construction of tagging rules for Polish [15]. Realising the difficulty of discovering tagging rules by hand, we wanted to develop a process of automatic acquisition of rules from the IPI PAN Corpus.…”

Section: Introductionmentioning

confidence: 99%

A Rule-Based Tagger for Polish Based on Genetic Algorithm

Piasecki

Gaweł

Advances in Soft Computing

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In the paper an approach to the construction of rule-based morphosyntactic tagger for Polish is proposed. The core of the tagger are modules of rules (classification systems), acquired from the IPI PAN corpus by application of Genetic Algorithms. Each module is specialised in making decisions concerning different parts of a tag (a structure of attributes). The acquired rules are combined with linguistic rules made by hand and memory-based rules acquired also from the corpus. The construction of the tagger and experiments concerning its properties are also presented in the paper.

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Forging Agreement: Morphological Disambiguation of Noun Phrases

Hinrichs

Trushkina

2004

Res Lang Comput

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The research reported here is part of a larger project on the development of a robust dependency parsing scheme GRIP (GeRman Incremental Parsing) that uses the Xerox Incremental Deep Parsing System and provides syntactic annotation in an incremental fashion. It is shown that morphological disambiguation is a crucial step in narrowing down the search space for the correct assignment of dependency structures. Customized disambiguation rules of XIP provide the necessary computational tools to efficiently carry out morphological disambiguation for languages with rich inflectional morphology such as German. A quantitative evaluation on a German test corpus shows that application of XIP disambiguation rules in conjunction with heuristics about the syntactic context of German NPs yields unique morphological analyses in 77.08% of all cases with a precision of 98.93%. This result is achieved by a novel constraint-based approach that integrates phrase-internal concord rules and phrase-external syntactic heuristics into one uniform architecture.

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Grammatical Agreement and Automatic Morphological Disambiguation of Inflectional Languages

Cited by 4 publications

References 2 publications

Context-based morphological disambiguation with random fields

Context-based morphological disambiguation with random fields

A Rule-Based Tagger for Polish Based on Genetic Algorithm

Forging Agreement: Morphological Disambiguation of Noun Phrases

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