C4.5: programs for machine learning
C4.5: programs for machine learning
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Information Retrieval
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Negation of protein–protein interactions
Bioinformatics
Negation recognition in medical narrative reports
Information Retrieval
The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A combined memory-based semantic role labeler of English
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Syntactic dependency based heuristics for biological event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
A survey on the role of negation in sentiment analysis
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
A unified framework for scope learning via simplified shallow semantic parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning the scope of negation via shallow semantic parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Retrieval of similar electronic health records using UMLS concept graphs
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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
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
UCM-2: a rule-based approach to infer the scope of negation via dependency parsing
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
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
Improving speculative language detection using linguistic knowledge
ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics
| Hi-index | 0.00 |
In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of these negation signals. Our approach to negation detection differs in two main aspects from existing research on negation. First, we focus on finding the scope of negation signals, instead of determining whether a term is negated or not. Second, we apply supervised machine learning techniques, whereas most existing systems apply rule-based algorithms. As far as we know, this way of approaching the negation scope finding task is novel.