TAILIEUCHUNG - Báo cáo khoa học: "Using Machine Learning Techniques to Interpret WH-questions"

We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user’s informational goals. We report on different aspects of the predictive performance of our models, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables. . | Using Machine Learning Techniques to Interpret WH-questions Ingrid Zukerman School of Computer Science and Software Engineering Monash University Clayton Victoria 3800 AUSTRALIA ingrid@ Eric Horvitz Microsoft Research One Microsoft Way Redmond WA 98052 USA horvitz@ Abstract We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. These models which are built from shallow linguistic features of questions are employed to predict target variables which represent a user s informational goals. We report on different aspects of the predictive performance of our models including the influence of various training and testing factors on predictive performance and examine the relationships among the target variables. 1 Introduction The growth in popularity of the Internet highlights the importance of developing machinery for generating responses to queries targeted at large unstructured corpora. At the same time the access of World Wide Web resources by large numbers of users provides opportunities for collecting and leveraging vast amounts of data about user activity. In this paper we describe research on exploiting data collected from logs of users queries in order to build models that can be used to infer users informational goals from queries. We describe experiments which use supervised machine learning techniques to build statistical models of questions posed to the Web-based En-carta encyclopedia service. We focus on models and analyses of complete questions phrased in English. These models predict a user s informational goals from shallow linguistic features of questions obtained from a natural language parser. We decompose these goals into 1 the type of information requested by the user . definition value of an attribute explanation for an event 2 the topic focal point and additional restrictions posed by the question and 3 the level of detail of the answer The

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