%0 Journal Article %1 WaltingerBreuingWachsmuth12ki %A Waltinger, Ulli %A Breuing, Alexa %A Wachsmuth, Ipke %D 2012 %J KI -- Künstliche Intelligenz %K v1205 springer paper ai user interface multimodal interaction adaptive language processing answer information retrieval agent learn %N 4 %P 381-390 %R 10.1007/s13218-012-0208-1 %T Connecting Question Answering and Conversational Agents %V 26 %X Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided by the questions. Thereby we focus on improving the conversational abilities of existing interactive interfaces by enhancing their underlying QA systems in terms of response time and correctness. As a result, we are able to introduce a method based on a tripartite contextualization. First, we present a comprehensive question classification experiment based on machine learning using two different datasets and various feature sets for the German language. Second, we propose a method for detecting the focus chunk of a given question, that is, for identifying which part of the question is fundamentally relevant to the answer and which part refers to a specification of it. Third, we investigate how to identify and label the topic of a given question by means of a human-judgment experiment. We show that the resulting contextualization method contributes to an improvement of existing question answering systems and enhances their application within interactive scenarios.

@article{WaltingerBreuingWachsmuth12ki, abstract = {Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided by the questions. Thereby we focus on improving the conversational abilities of existing interactive interfaces by enhancing their underlying QA systems in terms of response time and correctness. As a result, we are able to introduce a method based on a tripartite contextualization. First, we present a comprehensive question classification experiment based on machine learning using two different datasets and various feature sets for the German language. Second, we propose a method for detecting the focus chunk of a given question, that is, for identifying which part of the question is fundamentally relevant to the answer and which part refers to a specification of it. Third, we investigate how to identify and label the topic of a given question by means of a human-judgment experiment. We show that the resulting contextualization method contributes to an improvement of existing question answering systems and enhances their application within interactive scenarios.}, added-at = {2012-06-11T09:43:18.000+0200}, author = {Waltinger, Ulli and Breuing, Alexa and Wachsmuth, Ipke}, biburl = {https://www.bibsonomy.org/bibtex/2b98738855cf050c9d8edbe50120d5e8b/flint63}, doi = {10.1007/s13218-012-0208-1}, file = {SpringerLink:2012/WaltingerBreuingWachsmuth12ki.pdf:PDF}, groups = {public}, interhash = {663992cd6995c4150a0cb64e9ac07fec}, intrahash = {1d1dbd939b7bc729ef7fda19312af86c}, issn = {0933-1875}, journal = {KI -- K{\"u}nstliche Intelligenz}, keywords = {v1205 springer paper ai user interface multimodal interaction adaptive language processing answer information retrieval agent learn}, number = 4, pages = {381-390}, timestamp = {2012-11-21T10:27:27.000+0100}, title = {Connecting Question Answering and Conversational Agents}, username = {flint63}, volume = 26, year = 2012 }