Learning Features that Predict Cue Usage

Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. We apply a machine learning program, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. Our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.

[1]  Diane J. Litman,et al.  Cue Phrase Classification Using Machine Learning , 1996, J. Artif. Intell. Res..

[2]  Raymond J. Mooney,et al.  Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning , 1996, EMNLP.

[3]  Diane J. Litman,et al.  Cue Phrase Classification Using Machine Learning , 1996, J. Artif. Intell. Res..

[4]  Alistair Knott,et al.  A data-driven methodology for motivating a set of coherence relations , 1996 .

[5]  Barbara Di Eugenio,et al.  Learning Micro-Planning Rules for Preventive Expressions , 1996, INLG.

[6]  Johanna D. Moore,et al.  Investigating Cue Selection and Placement in Tutorial Discourse , 1995, ACL.

[7]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[8]  James H. Martin,et al.  Expressing Rhetorical Relations in Instructional Text: A Case Study of the Purposes Relation , 1995, Comput. Linguistics.

[9]  Kathleen McKeown,et al.  Emergent Linguistic Rules from inducing Decision Trees: Disambiguating Discourse Clue Words , 1994, AAAI.

[10]  Johanna D. Moore,et al.  DPOCL: A Principled Approach To Discourse Planning , 1994, INLG.

[11]  A. Graesser,et al.  The impact of connectives on the memory for expository texts , 1993 .

[12]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[13]  Manfred Stede,et al.  Customizing RST for the Automatic Production of Technical Manuals , 1992, NLG.

[14]  Kathleen McKeown,et al.  Text generation: using discourse strategies and focus constraints to generate natural language text , 1985 .

[15]  Michael Elhadad,et al.  A Contrastive Evaluation of Functional Unification Grammar for Surface Language Generation: A Case Study in Choice of Connectives , 1991 .

[16]  William C. Mann,et al.  Natural Language Generation in Artificial Intelligence and Computational Linguistics , 1990 .

[17]  Michael Elhadad,et al.  Generating Connectives , 1990, COLING.

[18]  Clarisse Sieckenius de Souza,et al.  Getting the message across in RST-based text generation , 1990 .

[19]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[20]  Susan R Goldman.  The Role of Sequence Markers on Reading and Recall: A Comparison of Native and Nonnative English Speakers , 1988 .

[21]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

[22]  Cécile Paris,et al.  Functional Unification Grammar Revisited , 1987, ACL.

[23]  James F. Allen,et al.  A Plan Recognition Model for Subdialogues in Conversations , 1987, Cogn. Sci..

[24]  Candace L. Sidner,et al.  Attention, Intentions, and the Structure of Discourse , 1986, CL.

[25]  William C. Mann,et al.  Relational propositions in discourse , 1986 .

[26]  J. Hobbs.  On the coherence and structure of discourse , 1985 .

[27]  Robin Cohen,et al.  A Computational Theory of the Function of Clue Words in Argument Understanding , 1984, ACL.

[28]  Rachel Reichman-Adar,et al.  Extended Person-Machine Interface , 1984, Artif. Intell..