C4.5: programs for machine learning
C4.5: programs for machine learning
WordNet: a lexical database for English
Communications of the ACM
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Review sentiment scoring via a parse-and-paraphrase paradigm
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Dependency tree-based sentiment classification using CRFs with hidden variables
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
SemEval-2010 task 9: The interpretation of noun compounds using paraphrasing verbs and prepositions
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Disambiguating dynamic sentiment ambiguous adjectives
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
SentiFul: A Lexicon for Sentiment Analysis
IEEE Transactions on Affective Computing
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Building subjectivity lexicon(s) from scratch for essay data
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Generating targeted paraphrases for improved translation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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The property of idiomaticity vs. compositionality of a multiword expression traditionally pertains to the semantic interpretation of the expression. In this article, we consider this property as it applies to the expression's sentiment profile (relative degree of positivity, negativity, and neutrality). Thus, while heart attack is idiomatic in terms of semantic interpretation, the sentiment profile of the expression (strongly negative) can, in fact, be determined from the strongly negative profile of the head word. In this article, we (1) propose a way to measure compositionality of a multiword expression's sentiment profile, and perform the measurement on noun-noun compounds; (2) evaluate the utility of using sentiment profiles of noun-noun compounds in a sentence-level sentiment classification task. We find that the sentiment profiles of noun-noun compounds in test-taker essays tend to be highly compositional and that their incorporation improves the performance of a sentiment classification system.