Conference Paper

Experiments with Interactive Question-Answering.

DOI: 10.3115/1219840.1219866 Conference: ACL 2005, 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 25-30 June 2005, University of Michigan, USA
Source: DBLP


This paper describes a novel framework for interactive question-answering (Q/A) based on predictive questioning. Gen- erated off-line from topic representations of complex scenarios, predictive ques- tions represent requests for information that capture the most salient (and diverse) aspects of a topic. We present experimen- tal results from large user studies (featur- ing a fully-implemented interactive Q/A system named FERRET) that demonstrates that surprising performance is achieved by integrating predictive questions into the context of a Q/A dialogue.

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    • "Statistical measures here were introduced for automatically selecting a number of useful domain-dependent P-A templates, resulting in a completely unsupervised learning of the information structure given a corpus. Kiyota et al. [20]; Dzikovska et al. [21]; Harabagiu et al., [22] proved every P-A structure was not useful for information extraction and retrieval because the predicate argument (P-A) structure generated by a parser as a baseline. In fact, the useful information structure was dependent upon domains [5]. "
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    ABSTRACT: We present a novel scheme of sentence transformation for Indonesian medical question generation (ImeQG) system by utilizing effectively documents for information navigation. Through the ImeQG proposed method, we conducted a general procedure of dependency analysis for extract verbs and relevant phrases to generate natural sentences by applying transformation rules. For this purpose, we defined some P-A templates based on a statistical measure. An experimental evaluation in this proposed method showed 79.00% for precision, 87.80% for recall and 81.50% for F1. Keywords—sentence transformation, predicate argument structure, Indonesian medical sentence, QA-pairs.
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    • "Our proposed scheme solves this problem by using information extraction based on semantic parsing from web texts, without constructing an RDB. We adopt the predicateargument (P-A) structure generated by a parser as a baseline, but every P-A structure is not useful for information extraction and retrieval(Y.Kiyota et al., 2002; M.O.Dzikovska et al., 2003; S.Harabagiu et al., 2005). In fact, the useful information structure is dependent on domains. "
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    ABSTRACT: We present a novel scheme of spoken dialogue systems which uses the up-to-date information on the web. The scheme is based on information extraction which is defined by the predicate-argument (P-A) structure and realized by semantic parsing. Based on the information structure, the dialogue system can perform question answering and also proactive information presentation. Feasibility of this scheme is demonstrated with experiments using a domain of baseball news. In order to automatically select useful domain-dependent P-A templates, statistical measures are introduced, resulting to a completely unsupervised learning of the information structure given a corpus. Similarity measures of P-A structures are also introduced to select relevant information. An experimental evaluation shows that the proposed system can make more relevant responses compared with the conventional "bag-of-words" scheme.
    Proceedings of the SIGDIAL 2011 Conference, The 12th Annual Meeting of the Special Interest Group on Discourse and Dialogue, June 17-18, 2011, Oregon Science & Health University, Portland, Oregon, USA; 01/2011
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    • "HITIQA is designed to allow intelligence analysts and other users of information systems to pose questions in natural language and obtain relevant answers or the assistance [17]. With fully-implemented interactive QA system named FERRET, Sanda Harabagiu et al achieved a surprising performance by integrating predictive questions into the context of a QA dialogue [18]. Chun- Chia Wang et al proposes a repository-based Question Answering system for collaborative E-learning [19]. "
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    ABSTRACT: Personalized knowledge acquisition is very important for promoting learning efficiency within E-learning system. To achieve this, two key problems involved are acquiring user’s knowledge requirements and discovering the people that can meet the requirements. In this paper, we present two approaches to realize personalized knowledge acquisition. The first approach aims to mine what knowledge the student requires and to what degree. All the interactive logs, accumulated during question answering process, are taken into account to compute each student’s knowledge requirement. The second approach is to construct and analyze user network based on the interactive data, which aims to find potential contributors list. Each student’s potential contributors may satisfy his/her requirement timely and accurately. Then we design an experiment to implement the two approaches. In order to evaluate the performance of our approaches, we make an evaluation with the percentage of satisfying recommendations. The evaluation results show that our approaches can help each student acquire the knowledge that he/she requires efficiently.
    Journal of Computers 05/2010; 5(5). DOI:10.4304/jcp.5.5.709-716
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