%0 Book Section %1 GriolMolinaCallejas17p103 %A Griol, David %A Molina, Jose M. %A Callejas, Zoraida %B Big Data Management %C Cham %D 2017 %E Garcıa Márquez, Fausto Pedro %E Lev, Benjamin %I Springer %K 01624 springer paper enterprise business data pattern recognition analysis language processing speech user dialog interaction zzz.big zzz.sds %P 103--121 %R 10.1007/978-3-319-45498-6_6 %T Big Data for Conversational Interfaces: Current Opportunities and Prospects %X As conversational technologies develop, we demand more from them. For instance, we want our conversational assistants to be able to solve our queries in multiple domains, to manage information from different usually unstructured sources, to be able to perform a variety of tasks, and understand open conversational language. However, developing the resources necessary to develop systems with such capabilities demands much time and effort, as for each domain, task or language, data must be collected, annotated following an schema that is usually not portable, the models must be trained over the annotated data, and their accuracy must be evaluated. In recent years, there has been a growing interest in investigating alternatives to manual effort that allow exploiting automatically the huge amount of resources available in the web. In this chapter we describe the main initiatives to extract, process and contextualize information from these rich and heterogeneous sources for the various tasks involved in dialog systems, including speech processing, natural language understanding and dialog management. %@ 978-3-319-45497-9

@incollection{GriolMolinaCallejas17p103, abstract = {As conversational technologies develop, we demand more from them. For instance, we want our conversational assistants to be able to solve our queries in multiple domains, to manage information from different usually unstructured sources, to be able to perform a variety of tasks, and understand open conversational language. However, developing the resources necessary to develop systems with such capabilities demands much time and effort, as for each domain, task or language, data must be collected, annotated following an schema that is usually not portable, the models must be trained over the annotated data, and their accuracy must be evaluated. In recent years, there has been a growing interest in investigating alternatives to manual effort that allow exploiting automatically the huge amount of resources available in the web. In this chapter we describe the main initiatives to extract, process and contextualize information from these rich and heterogeneous sources for the various tasks involved in dialog systems, including speech processing, natural language understanding and dialog management.}, added-at = {2016-12-31T20:20:35.000+0100}, address = {Cham}, author = {Griol, David and Molina, Jose M. and Callejas, Zoraida}, biburl = {https://www.bibsonomy.org/bibtex/2d90dbdb7b10afee6d06425438fd93984/flint63}, booktitle = {Big Data Management}, crossref = {GarciaMarquezLev2017}, doi = {10.1007/978-3-319-45498-6_6}, editor = {Garc{\i}a M\'{a}rquez, Fausto Pedro and Lev, Benjamin}, file = {SpringerLink:2017/GriolMolinaCallejas17p103.pdf:PDF}, groups = {public}, interhash = {ec079bcf30017b074c475d1730042d72}, intrahash = {d90dbdb7b10afee6d06425438fd93984}, isbn = {978-3-319-45497-9}, keywords = {01624 springer paper enterprise business data pattern recognition analysis language processing speech user dialog interaction zzz.big zzz.sds}, pages = {103--121}, publisher = {Springer}, timestamp = {2017-07-13T17:39:29.000+0200}, title = {Big Data for Conversational Interfaces: Current Opportunities and Prospects}, username = {flint63}, year = 2017 }