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Cue Phrase Classification Using Machine Learning [article]

Diane J. Litman
1996 arXiv   pre-print
This paper explores the use of machine learning for classifying cue phrases as discourse or sentential.  ...  Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech.  ...  Acknowledgements I would like to thank William Cohen and Jason Catlett for their helpful comments regarding the use of cgrendel and C4.5, and Sandra Carberry, Rebecca Passonneau, and the three anonymous  ... 
arXiv:cmp-lg/9609003v1 fatcat:xiz6w4kh2vfedlxw452ycqm3sa

Cue Phrase Classification Using Machine Learning [article]

D. J. Litman
1996 arXiv   pre-print
This paper explores the use of machine learning for classifying cue phrases as discourse or sentential.  ...  Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech.  ...  Acknowledgements I would like to thank William Cohen and Jason Catlett for their helpful comments regarding the use of cgrendel and C4.5, and Sandra Carberry, Rebecca Passonneau, and the three anonymous  ... 
arXiv:cs/9609102v1 fatcat:qba62ixbi5aezelczbhzl4kbu4

Cue Phrase Classification Using Machine Learning

D. J. Litman
1996 The Journal of Artificial Intelligence Research  
This paper explores the use of machine learning for classifying cue phrases as discourse or sentential.  ...  Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech.  ...  Acknowledgements I w ould like to thank William Cohen and Jason Catlett for their helpful comments regarding the use of cgrendel and C4.5, and Sandra Carberry, Rebecca Passonneau, and the three anonymous  ... 
doi:10.1613/jair.327 fatcat:p3o5umnmgnc2zibcphy3n3dlau

Use of negation phrases in automatic sentiment classification of product reviews

Jin-Cheon Na, Christopher Khoo, Paul Horng Jyh Wu
2005 Library collections, acquisitions & technical services  
The study investigates the effectiveness of using a machine-learning algorithm, support vector machine (SVM), on various text features to classify on-line product reviews into recommended (positive sentiment  ...  The second part of the study investigated the use of negation phrase n-grams to improve classification accuracy. This approach increased the accuracy rate to 79.33%.  ...  The TFIDF weight has been used in many studies on topical text classification. Machine-learning methods A machine-learning method, support vector machine (SVM), was used in this study.  ... 
doi:10.1016/j.lcats.2005.04.007 fatcat:dsdvhvbevjdolg6cv4lpoqr44q

Use of negation phrases in automatic sentiment classification of product reviews

Jin-Cheon Na, Christopher Khoo, Paul Horng Jyh Wu
2005 Library collections, acquisitions & technical services  
The study investigates the effectiveness of using a machine-learning algorithm, support vector machine (SVM), on various text features to classify on-line product reviews into recommended (positive sentiment  ...  The second part of the study investigated the use of negation phrase n-grams to improve classification accuracy. This approach increased the accuracy rate to 79.33%.  ...  The TFIDF weight has been used in many studies on topical text classification. Machine-learning methods A machine-learning method, support vector machine (SVM), was used in this study.  ... 
doi:10.1080/14649055.2005.10766050 fatcat:3meu3dlpefcizj4hcjdlcda42e

Combining multiple knowledge sources for discourse segmentation

Diane J. Litman, Rebecca J. Passonneau
1995 Proceedings of the 33rd annual meeting on Association for Computational Linguistics -  
When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).  ...  We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning.  ...  Machine Learning We use the machine learning program C4.5 (Quinlan, 1993) to automatically develop segmentation algorithms from our corpus of coded narratives, where each potential boundary site has  ... 
doi:10.3115/981658.981673 dblp:conf/acl/LitmanP95 fatcat:n3aeitji3neojigls2wwxzhiee

Classifying Cue Phrases in Text and Speech Using Machine Learning [article]

Diane J. Litman
1994 arXiv   pre-print
Two machine learning programs (Cgrendel and C4.5) are used to induce classification rules from sets of pre-classified cue phrases and their features.  ...  This paper explores the use of machine learning for classifying cue phrases as discourse or sentential.  ...  Acknowledgments I would like to thank William Cohen and Jason Catlett for help in using cgrendel and C4.5, and William Cohen, Ido Dagan, Julia Hirschberg, and Eric Siegel for comments on an earlier version  ... 
arXiv:cmp-lg/9405014v1 fatcat:tqfadlvsmbbvrkurumkqg3yra4

Combining Multiple Knowledge Sources for Discourse Segmentation [article]

Diane J. Litman, Rebecca J. Passonneau
1995 arXiv   pre-print
When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).  ...  We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning.  ...  Machine Learning We use the machine learning program C4.5 (Quinlan, 1993) to automatically develop segmentation algorithms from our corpus of coded narratives, where each potential boundary site has  ... 
arXiv:cmp-lg/9505025v1 fatcat:wdom54wgefcqdcvpft2qssdawu

Error Analysis of Dialogue Act Classification [chapter]

Nick Webb, Mark Hepple, Yorick Wilks
2005 Lecture Notes in Computer Science  
We have built a simple dialogue act classifier based on purely intrautterance features -principally word n-gram cue phrases.  ...  We have performed an error analysis of the output of our classifier, with a view to casting light both on the system's performance, and on the da classification scheme itself.  ...  Using Cue Phrases in Classification The selected cue phrases are used directly in classifying previously unseen utterances in the following manner.  ... 
doi:10.1007/11551874_58 fatcat:ulm35ajpqndv3g6ksdkktlwmxu

Identifying Data Sharing in Biomedical Literature

Heather Piwowar, Wendy Chapman
2008 Nature Precedings  
Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision.  ...  We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles.  ...  Overall Machine learning classifiers: Machine learning classification performance using matches to the manual regular expression patterns as features achieved much higher precision than any of the regular  ... 
doi:10.1038/npre.2008.1721.1 fatcat:4gdsy4bb6zg4fexfcvjaoabvb4

Identifying Data Sharing in Biomedical Literature

Heather Piwowar, Wendy Chapman
2008 Nature Precedings  
Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision.  ...  We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles.  ...  Overall Machine learning classifiers: Machine learning classification performance using matches to the manual regular expression patterns as features achieved much higher precision than any of the regular  ... 
doi:10.1038/npre.2008.1721.2 fatcat:3qhcvwk54rdffpngpif2qpj2iy

Page 129 of Computational Linguistics Vol. 23, Issue 1 [page]

1997 Computational Linguistics  
Note that although not all available features are used in the tree, the included features represent three of the four general types of knowledge (prosody, cue phrases, and noun phrases).  ...  Furthermore, even when machine learning does not use global.pro (as with the “Learning 2” algorithm discussed below), performance does not suffer.  ... 

Cue-based assertion classification for Swedish clinical text—Developing a lexicon for pyConTextSwe

Sumithra Velupillai, Maria Skeppstedt, Maria Kvist, Danielle Mowery, Brian E. Chapman, Hercules Dalianis, Wendy W. Chapman
2014 Artificial Intelligence in Medicine  
Methods and material: We integrated cues from four external lexicons, along with generated inflections and combinations. We used subsets of a clinical corpus in Swedish.  ...  For the binary classifications existence yes/no and uncertainty yes/no, final system performance was 97%/87% and 78%/86% F-score, respectively.  ...  Negation cue detection and scope Researchers have used both rule-based and machine-learning approaches to study negation cues and their scope [6, [20] [21] [22] .  ... 
doi:10.1016/j.artmed.2014.01.001 pmid:24556644 pmcid:PMC4104142 fatcat:3viq4a32dnbxlmtwdswxb6eeui

A Machine Learning Approach to the Classification of Dialogue Utterances [article]

Toine Andernach (Parlevink Group, Department of Computer Science, University of Twente, The Netherlands)
1996 arXiv   pre-print
Instead of relying on subjective judgments for the tasks of finding classes and rules, we opt for using machine learning techniques to guarantee objectivity.  ...  The purpose of this paper is to present a method for automatic classification of dialogue utterances and the results of applying that method to a corpus.  ...  from a training corpus by machine learning techniques.  ... 
arXiv:cmp-lg/9607022v1 fatcat:shkyifqjejbqrl3gjzhdx53fzq

RelHunter: a machine learning method for relation extraction from text

Eraldo R. Fernandes, Ruy L. Milidiú, Raúl P. Rentería
2010 Journal of the Brazilian Computer Society  
We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter's key idea is to model the target structures as a relation over entities.  ...  We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing.  ...  Punyakanok and Roth [21] divide this problem into three machine learning subtasks.  ... 
doi:10.1007/s13173-010-0018-y fatcat:rjeytyclwjgj7ea46mvbn6wszy
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