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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| 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): |  Urheberrechtlich geschützt |