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Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorization

  • We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on 5 different food categorization tasks.

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  • Wiegand_Roth_Klakow_Combining_pb_and_d_Similarity_for_Noun_Categorization_2015.pdf
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Metadaten
Author:Michael WiegandGND, Benjamin Roth, Dietrich Klakow
URN:urn:nbn:de:bsz:mh39-87479
DOI:https://doi.org/10.1007/978-3-319-19581-0_5
ISBN:978-3-319-19580-3
Parent Title (English):Natural Language Processing and Information Systems. Proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, Passau, Germany, June 17–19, 2015
Series (Serial Number):Lecture Notes in Computer Science (9103)
Publisher:Springer
Place of publication:Cham
Editor:Chris Biemann, Siegfried Handschuh, André Freitas, Farid Meziane, Elisabeth Métais
Document Type:Conference Proceeding
Language:English
Year of first Publication:2015
Date of Publication (online):2019/04/02
Tag:Food item; Graph cluster; Neighbour classifier; Relation extraction; Unconnected node
GND Keyword:Computerlinguistik; Grafische Darstellung; Information Extraction; Lebensmittel; Maschinelles Lernen
First Page:64
Last Page:72
Note:
Dieser Beitrag ist aus urheberrechtlichen Gründen online nicht frei zugänglich.
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:nein
Licence (German):Es gilt das UrhG