A maximum entropy approach to natural language processing
Computational Linguistics
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Textual demand analysis: detection of users' wants and needs from opinions
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A semi-automated approach to building text summarisation classifiers
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Our general research aim is to extract the actual intentions of persons when they respond to open-ended questionnaires. These intentions include the desire to make requests, complaints, expressions of resignation and so forth, but here we focus on extracting the intention to make a request. To do so, we first have to judge whether their responses contain the intent to make a request. Therefore, as a first step, we have developed a criterion for judging the existence of request intentions in responses. This criterion, which is based on paraphrasing, is described in detail in this paper. Our assumption is that a response with request intentions can be paraphrased into a typical request expression, e.g., "I would like to ...", while responses without request are not paraphrasable. The criterion is evaluated in terms of objectivity, reproducibility and effectiveness. Objectivity is demonstrated by showing that machine learning methods can learn the criterion from a set of intention-tagged data, while reproducibility, that the judgments of three annotators are reasonably consistent, and effectiveness, that judgments based not on the criterion but on intuition do not agree. This means the criterion is necessary to achieve reproducibility. These experiments indicate that the criterion can be used to judge the existence of request intentions in responses reliably.