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1
Lexical acquisition for opinion inference: A sense-level lexicon of benefactive and malefactive events
In: http://www.aclweb.org/anthology/W/W14/W14-2618.pdf (2014)
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2
Multilingual Sentiment and Subjectivity Analysis
In: http://www.cs.unt.edu/%7Erada/papers/banea.chap11.pdf (2011)
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3
Recognizing stances in ideological on-line debates
In: http://people.cs.pitt.edu/~wiebe/pubs/papers/naacl2010wkshop.pdf (2010)
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4
Content of Linguistic Annotation: Standards and Practices (CLASP) Research Activities and Findings
In: http://www.cims.nyu.edu/~meyers/SIGANN-wiki/wiki/images/b/b2/FinalClasp.pdf (2010)
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5
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
In: http://www.aclweb.org/anthology-new/J/J09/J09-3003.pdf (2009)
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6
Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis
In: http://www.cs.pitt.edu/~wiebe/pubs/papers/wilsoncl09.pdf (2009)
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7
Exploiting semantic role resources for preposition disambiguation
In: http://www.cs.nmsu.edu/~tomohara/ohara-cl-35-2-jun09.pdf (2009)
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8
An earley parsing algorithm for range concatenation grammars [Online resource]
In: http://www.sfs.uni-tuebingen.de/~lk/papers/KallmMaierParm-ACL09.pdf ; (in:) Proceedings of ACL. - Singapore, 2009 (2009)
Linguistik-Repository
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9
A bootstrapping method for building subjectivity lexicons for languages with scarce resources
In: http://people.cs.pitt.edu/~wiebe/pubs/papers/lrec2008.pdf (2008)
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10
Detecting arguing and sentiment in meetings
In: http://www.sigdial.org/workshops/workshop8/Proceedings/SIGdial05.pdf (2008)
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11
Multilingual subjectivity analysis using machine translation
In: http://www.cs.unt.edu/~rada/papers/banea.emnlp08.pdf (2008)
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12
Finding the sources and targets of subjective expressions
In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/709_paper.pdf (2008)
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13
Learning Multilingual Subjective Language via Cross-Lingual Projections
In: http://aclweb.org/anthology-new/P/P07/P07-1123.pdf (2007)
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14
Annotation compatibility working group report [Online resource]
In: http://jones.ling.indiana.edu/~skuebler/papers/frontiers06.pdf ; Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006 (Sydney 2006). (2006), 38-53
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15
Exploiting subjectivity classification to improve information extraction
In: http://www.aaai.org/Papers/AAAI/2005/AAAI05-175.pdf (2005)
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16
Combining lowlevel and summary representations of opinions for multiperspective question answering
In: http://www.aaai.org/Papers/Symposia/Spring/2003/SS-03-07/SS03-07-004.pdf (2004)
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17
Combining lowlevel and summary representations of opinions for multiperspective question answering
In: http://www.cs.cornell.edu/home/cardie/papers/aaai-ss-summary-rep-03.pdf (2004)
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18
Learning subjective language
In: http://people.cs.pitt.edu/~wiebe/pubs/papers/tr02100.pdf (2004)
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19
Preposition Semantic Classification via TREEBANK and FRAMENET
In: http://www.cs.nmsu.edu/~cssem/spring03/ohara-preposition-classification-acl03.pdf (2003)
Abstract: This paper reports on experiments in classifying the semantic role annotations assigned to prepositional phrases in both PENN TREEBANK (version II) and FRAMENET (version 0.75). In both cases, experiments are done to see how the prepositions can be classified given the dataset’s role inventory, using standard word-sense disambiguation features, such as the parts of speech of surrounding words, and collocations indicative of the particular roles. In addition to using traditional word collocations, the experiments incorporate class-based collocations in the form of WordNet hypernyms. Separate classifiers are produced for each preposition. For TreeBank, the wordcollocations achieve slightly better performance: 78.5 % versus 77.4%. However, for FrameNet, the combined collocations achieve better performance: 70.3 % versus 68.5 % Furthermore when classifying all the TreeBanks prepositions together, the combined yields a noticable gain at 85.8% accuracy versus 81.3 % for word-only collocations.
URL: http://www.cs.nmsu.edu/~cssem/spring03/ohara-preposition-classification-acl03.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.3067
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20
Classifying functional relations in Factotum via WordNet hypernym associations
In: http://www.cs.nmsu.edu/~tomohara/ohara-factotum-roles-cicling03.pdf (2003)
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