Getting computers to talk like you and me
Getting computers to talk like you and me
Attention, intentions, and the structure of discourse
Computational Linguistics
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Competitively evolving decision trees against fixed training cases for natural language processing
Advances in genetic programming
Classifying cue phrases in text and speech using machine learning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Emergent linguistic rules from inducing decision trees: disambiguating discourse clue words
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Corpus-driven knowledge acquisition for discourse analysis
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning approaches for natural language processing
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
Discourse segmentation by human and automated means
Computational Linguistics
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A computational theory of the function of clue words in argument understanding
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Now let's talk about now: identifying cue phrases intonationally
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Acquiring disambiguation rules from text
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Combining multiple knowledge sources for discourse segmentation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Investigating cue selection and placement in tutorial discourse
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Disambiguating cue phrases in text and speech
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An evaluation method of words tendency depending on time-series variation and its improvements
Information Processing and Management: an International Journal
A Comparison of Rule-Based and Machine Learning Methods for Identifying Non-nominal It
NLP '00 Proceedings of the Second International Conference on Natural Language Processing
An Empirical Approach to Discourse Markers by Clustering
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
The rhetorical parsing of unrestricted texts: a surface-based approach
Computational Linguistics
Empirical studies in discourse
Computational Linguistics
Discourse segmentation by human and automated means
Computational Linguistics
Surface-marker-based dialog modelling: A progress report on the MAREDI project
Natural Language Engineering
Learning features that predict cue usage
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Constituent-based accent prediction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Experiments with Learning Parsing Heuristics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An evaluation method of words tendency using decision tree
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Extracting conceptual relationships from specialized documents
Data & Knowledge Engineering - Special issue: ER 2002
Automated story capture from conversational speech
Proceedings of the 3rd international conference on Knowledge capture
Mining discourse markers for Chinese textual summarization
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
Using automatically labelled examples to classify rhetorical relations: An assessment
Natural Language Engineering
An automatic extraction method of word tendency judgement for specific subjects
International Journal of Computer Applications in Technology
Mining discourse markers for Chinese textual summarization
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Cueing the virtual storyteller: analysis of cue phrase usage in fairy tales
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Individual and domain adaptation in sentence planning for dialogue
Journal of Artificial Intelligence Research
Automatic identification of discourse markers in dialogues: An in-depth study of like and well
Computer Speech and Language
Affirmative cue words in task-oriented dialogue
Computational Linguistics
An improvement approach for word tendency using decision tree
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
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Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. 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. Machine learning is shown to be an effective technique for not only automating the generation of classification models, but also for improving upon previous results. When compared to manually derived classification models already in the literature, the learned models often perform with higher accuracy and contain new linguistic insights into the data. In addition, the ability to automatically construct classification models makes it easier to comparatively analyze the utility of alternative feature representations of the data. Finally, the ease of retraining makes the learning approach more scalable and flexible than manual methods.