Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Exploring hedge identification in biomedical literature
Journal of Biomedical Informatics
Learning the scope of hedge cues in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Automatic extraction of lexico-syntactic patterns for detection of negation and speculation scopes
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Hi-index | 0.00 |
In this paper, we present a machine learning approach that detects hedge cues and their scope in biomedical texts. Identifying hedged information in texts is a kind of semantic filtering of texts and it is important since it could extract speculative information from factual information. In order to deal with the semantic analysis problem, various evidential features are proposed and integrated through a Conditional Random Fields (CRFs) model. Hedge cues that appear in the training dataset are regarded as keywords and employed as an important feature in hedge cue identification system. For the scope finding, we construct a CRF-based system and a syntactic pattern-based system, and compare their performances. Experiments using test data from CoNLL-2010 shared task show that our proposed method is robust. F-score of the biological hedge detection task and scope finding task achieves 86.32% and 54.18% in in-domain evaluations respectively.