Convolution Kernels for Subjectivity Detection
- In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.
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| Author: | Michael WiegandGND, Dietrich Klakow |
|---|---|
| URN: | urn:nbn:de:bsz:mh39-85032 |
| Handle: | http://hdl.handle.net/10062/17338 |
| ISSN: | 1736-6305 |
| Parent Title (English): | Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011), May 11-13, 2011, Riga, Latvia |
| Series (Serial Number): | NEALT Proceedings Series (11) |
| Publisher: | Northern European Association for Language Technology |
| Place of publication: | Uppsala |
| Editor: | Bolette Sandford Pedersen, Gunta Nešpore, Inguna Skadiņa |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2011 |
| Date of Publication (online): | 2019/02/21 |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| Tag: | Sentimentanalyse |
| GND Keyword: | Computerlinguistik; Maschinelles Lernen; Natürliche Sprache; Subjektivität; Text Mining |
| First Page: | 254 |
| Last Page: | 261 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Linguistics-Classification: | Computerlinguistik |
| Licence (German): |  Urheberrechtlich geschützt |