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Growing trees from morphs: Towards data-driven morphological parsing

  • We present a quantitative approach to disambiguating flat morphological analyses and producing more deeply structured analyses. Based on existing morphological segmentations, possible combinations of resulting word trees for the next level are filtered first by criteria of linguistic plausibility and then by weighting procedures based on the geometric mean. The frequencies for weighting are derived from three different sources (counts of morphs in a lexicon, counts of largest constituents in a lexicon, counts of token frequencies in a corpus) and can be used either to find the best analysis on the level of morphs or on the next higher constituent level. The evaluation shows that for this task corpus-based frequency counts are slightly superior to counts of lexical data.

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Metadaten
Author:Petra SteinerORCiD, Josef RuppenhoferGND
URN:urn:nbn:de:bsz:mh39-52323
URL:http://www.gscl.org/proceedings/2015/
Parent Title (English):Proceedings of the Int. Conference of the German Society for Computational Linguistics and Language Technology, Sep 30–Oct 2 2015
Publisher:Gesellschaft für Sprachtechnologie and Computerlinguistik
Document Type:Conference Proceeding
Language:English
Year of first Publication:2015
Date of Publication (online):2016/09/01
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:morphological analyses; word trees
GND Keyword:Computerlinguistik; Deutsch; Morphemanalyse; Segmentierung; Worthäufigkeit
First Page:49
Last Page:57
Dewey Decimal Classification:400 Sprache / 410 Linguistik
BDSL-Classification:Textwissenschaft
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):Es gilt das UrhG