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Learning theories for noun-phrase sentiment composition

Petrakis, S; Klenner, M (2011). Learning theories for noun-phrase sentiment composition. In: 8th International NLPCS Workshop, Kopenhagen, 20 August 2011 - 21 August 2011. Samfundslitteratur, 179-188.

Abstract

The work presented here is an approach to Sentiment Analy-
sis from a rule-based, compositional perspective. The proposed approach is characterized by three major points: (a) rules are automatically learned from annotated corpora using Inductive Logic Programming and represented as Prolog sets of clauses, (b) the focus is on the noun-phrase (NP) level, and (c) learning is performed on deep-parsed structures. We describe the process of annotating a collection of some 3000 German NPs of medium to quite complex structure, as well as the empirical evaluation of
our implementation, in comparison with commonly used classifiers and a handcrafted rule-based system.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:21 August 2011
Deposited On:03 Feb 2012 08:19
Last Modified:03 Aug 2021 15:10
Publisher:Samfundslitteratur
Series Name:Copenhagen Studies of Language
Number:41
ISSN:0905-2469
ISBN:978-87-593-1615-3
OA Status:Green
Related URLs:http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=15082&copyownerid=8741
http://www.samfundslitteratur.dk/Visning-af-titel.242.0.html?&cHash=6a63d862c5133ff29abbf34173735bd9&ean=9788759316153
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