Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Without a 'doubt'?: unsupervised discovery of downward-entailing operators
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
The viability of web-derived polarity lexicons
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Sentiment analysis of citations using sentence structure-based features
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Semantic representation of negation using focus detection
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Getting the most out of transition-based dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Enriching ontologies by learned negation: or how to teach ontologies vegetarianism
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Mining subjective knowledge from customer reviews: a specific case of irony detection
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Compositional matrix-space models for sentiment analysis
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Inferring the scope of negation in biomedical documents
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
A machine-learning approach to negation and speculation detection in clinical texts
Journal of the American Society for Information Science and Technology
Modality and negation: An introduction to the special issue
Computational Linguistics
Speculation and negation: Rules, rankers, and the role of syntax
Computational Linguistics
Making objective decisions from subjective data: Detecting irony in customer reviews
Decision Support Systems
Unsupervised detection of downward-entailing operators by maximizing classification certainty
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Leveraging relationships in social networks for sentiment analysis
Proceedings of the 18th Brazilian symposium on Multimedia and the web
UABCoRAL: a preliminary study for resolving the scope of negation
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
UCM-I: a rule-based syntactic approach for resolving the scope of negation
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
UiO2: sequence-labeling negation using dependency features
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
UMichigan: a conditional random field model for resolving the scope of negation
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
FBK: exploiting phrasal and contextual clues for negation scope detection
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
A hybrid approach to finding negated and uncertain expressions in biomedical documents
Proceedings of the 2nd international workshop on Managing interoperability and compleXity in health systems
Bootstrapping polarity classifiers with rule-based classification
Language Resources and Evaluation
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Automatic detection of linguistic negation in free text is a critical need for many text processing applications, including sentiment analysis. This paper presents a negation detection system based on a conditional random field modeled using features from an English dependency parser. The scope of negation detection is limited to explicit rather than implied negations within single sentences. A new negation corpus is presented that was constructed for the domain of English product reviews obtained from the open web, and the proposed negation extraction system is evaluated against the reviews corpus as well as the standard BioScope negation corpus, achieving 80.0% and 75.5% F1 scores, respectively. The impact of accurate negation detection on a state-of-the-art sentiment analysis system is also reported.