A freely available morphological analyzer, disambiguator and context sensitive lemmatizer for German

Lezius, Wolfgang; Rapp, Reinhard; Wettler, Manfred

doi:10.3115/980691.980692

Cited by 27 publications

(18 citation statements)

References 6 publications

(7 reference statements)

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“…(According to Lezius, Rapp, & Wettler, 1998, 93% of the tokens of a German text had only one lemma.) Although we had a context-sensitive lemmatizer for German available (Lezius, Rapp, & Wettler, 1998), this was not the case for English, so for reasons of symmetry we decided not to use the context feature. I In cases in which an ambiguous word can be both a content and a function word (e.g., can), preference was given to those interpretations that appeared to occur more frequently.…”

Section: Pre-processingmentioning

confidence: 99%

Automatic identification of word translations from unrelated English and German corpora

Rapp¹

1999

Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics -

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Algorithms for the alignment of words in translated texts are well established. However, only recently new approaches have been proposed to identify word translations from non-parallel or even unrelated texts. This task is more difficult, because most statistical clues useful in the processing of parallel texts cannot be applied to non-parallel texts. Whereas for parallel texts in some studies up to 99% of the word alignments have been shown to be correct, the accuracy for non-parallel texts has been around 30% up to now. The current study, which is based on the assumption that there is a correlation between the patterns of word co-occurrences in corpora of different languages, makes a significant improvement to about 72% of word translations identified correctly.

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Section: Pre-processingmentioning

confidence: 99%

Automatic identification of word translations from unrelated English and German corpora

Rapp¹

1999

Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics -

Self Cite

263

273

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“…For example, the accusative and plural markers must occur in a specific order after a word ending morpheme, and a word cannot end with a root or affix. Maximal morpheme matching has been incorporated into morphological analysis systems for other agglutinative languages, including German (for compound nouns only) (Lezius et al, 1998) and Turkish (Solak and Oflazer, 1992). Thus, it is valuable to directly compare McBurnett's approach to other approaches of Esperanto morphological segmentation.…”

Section: Esperantomentioning

confidence: 99%

An Algorithm for Morphological Segmentation of Esperanto Words

Guinard¹

2016

The Prague Bulletin of Mathematical Linguistics

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Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of-speech information) is a useful preprocessing step in many natural language processing applications, especially for synthetic languages. Compound words from the constructed language Esperanto are formed by straightforward agglutination, but for many words, there is more than one possible sequence of component morphemes. However, one segmentation is usually more semantically probable than the others. This paper presents a modified n-gram Markov model that finds the most probable segmentation of any Esperanto word, where the model's states represent morpheme part-of-speech and semantic classes. The overall segmentation accuracy was over 98% for a set of presegmented dictionary words.

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“…investigates how off-the-shelf POS taggers can be combined to better cope with text material that differs from the type of text the taggers were originally trained, and for which there are no readily available training corpora. The author uses three taggers for German -TreeTagger ), Morphy (Lezius et al 1998) and QTAG . He evaluates the taggers and creates a list of differences between taggers and a hypothesis about which parameters were likely to influence tagger performance.…”

Section: Combining Pos-taggersmentioning

confidence: 99%

A resource-light approach to morpho-syntactic tagging

Feldman¹,

Hana²

2010

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Morpho-syntactic tagging is the process of assigning part of speech (POS), case, number, gender, and other morphological information to each word in a corpus. Morpho-syntactic tagging is an important step in natural language processing. Corpora that have been morphologically tagged are very useful both for linguistic research, e.g. finding instances or frequencies of particular constructions in large corpora, and for further computational processing, such as syntactic parsing, speech recognition, stemming, and word-sense disambiguation, among others. Despite the importance of morphological tagging, there are many languages that lack annotated resources. This is almost inevitable because these resources are costly to create. But, as described in this thesis, it is possible to avoid this expense. This thesis describes a method for transferring annotation from a morphologically annotated corpus of a source language to a corpus of a related target language. Unlike unsupervised approaches that do not require annotated data at all and, as a consequence, lack precision, the approach proposed in this dissertation relies on linguistic knowledge, but avoids large-scale grammar engineering. The approach needs neither a parallel corpus nor a bilingual lexicon, and requires much less linguistic labor than the standard technology. This dissertation describes experiments with Russian, Czech, Polish, Spanish, Portuguese, and Catalan. However, the general method proposed can be applied to any fusional language.

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A freely available morphological analyzer, disambiguator and context sensitive lemmatizer for German

Cited by 27 publications

References 6 publications

Automatic identification of word translations from unrelated English and German corpora

Automatic identification of word translations from unrelated English and German corpora

An Algorithm for Morphological Segmentation of Esperanto Words

A resource-light approach to morpho-syntactic tagging

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