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)
|
|
BASE
|
|
Show details
|
|
2 |
Multilingual Sentiment and Subjectivity Analysis
|
|
|
|
In: http://www.cs.unt.edu/%7Erada/papers/banea.chap11.pdf (2011)
|
|
BASE
|
|
Show details
|
|
3 |
Recognizing stances in ideological on-line debates
|
|
|
|
In: http://people.cs.pitt.edu/~wiebe/pubs/papers/naacl2010wkshop.pdf (2010)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
7 |
Exploiting semantic role resources for preposition disambiguation
|
|
|
|
In: http://www.cs.nmsu.edu/~tomohara/ohara-cl-35-2-jun09.pdf (2009)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
10 |
Detecting arguing and sentiment in meetings
|
|
|
|
In: http://www.sigdial.org/workshops/workshop8/Proceedings/SIGdial05.pdf (2008)
|
|
BASE
|
|
Show details
|
|
11 |
Multilingual subjectivity analysis using machine translation
|
|
|
|
In: http://www.cs.unt.edu/~rada/papers/banea.emnlp08.pdf (2008)
|
|
BASE
|
|
Show details
|
|
12 |
Finding the sources and targets of subjective expressions
|
|
|
|
In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/709_paper.pdf (2008)
|
|
BASE
|
|
Show details
|
|
13 |
Learning Multilingual Subjective Language via Cross-Lingual Projections
|
|
|
|
In: http://aclweb.org/anthology-new/P/P07/P07-1123.pdf (2007)
|
|
BASE
|
|
Show details
|
|
15 |
Exploiting subjectivity classification to improve information extraction
|
|
|
|
In: http://www.aaai.org/Papers/AAAI/2005/AAAI05-175.pdf (2005)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
18 |
Learning subjective language
|
|
|
|
In: http://people.cs.pitt.edu/~wiebe/pubs/papers/tr02100.pdf (2004)
|
|
BASE
|
|
Show details
|
|
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
|
|
BASE
|
|
Hide details
|
|
20 |
Classifying functional relations in Factotum via WordNet hypernym associations
|
|
|
|
In: http://www.cs.nmsu.edu/~tomohara/ohara-factotum-roles-cicling03.pdf (2003)
|
|
BASE
|
|
Show details
|
|
|
|