Generating Exact- and Ranked Partially-Matched Answers to Questions in Advertisements

Taking advantage of the Web, many advertisements (ads for short) websites, which aspire to increase client's transactions and thus profits, offer searching tools which allow users to (i) post keyword queries to capture their information needs or (ii) invoke form-based interfaces to create queries by selecting search options, such as a price range, filled-in entries, check boxes, or drop-down menus. These search mechanisms, however, are inadequate, since they cannot be used to specify a natural-language query with rich syntactic and semantic content, which can only be handled by a question answering (QA) system. Furthermore, existing ads websites are incapable of evaluating arbitrary Boolean queries or retrieving partially-matched answers that might be of interest to the user whenever a user's search yields only a few or no results at all. In solving these problems, we present a QA system for ads, called CQAds, which (i) allows users to post a natural-language question Q for retrieving relevant ads, if they exist, (ii) identifies ads as answers that partially-match the requested information expressed in Q, if insufficient or no answers to Q can be retrieved, which are ordered using a similarity-ranking approach, and (iii) analyzes incomplete or ambiguous questions to perform the "best guess" in retrieving answers that "best match" the selection criteria specified in Q. CQAds is also equipped with a Boolean model to evaluate Boolean operators that are either explicitly or implicitly specified in Q, i.e., with or without Boolean operators specified by the users, respectively. CQAds is easy to use, scalable to all ads domains, and more powerful than search tools provided by existing ads websites, since its query-processing strategy retrieves relevant ads of higher quality and quantity. We have verified the accuracy of CQAds in retrieving ads on eight ads domains and compared its ranking strategy with other well-known ranking approaches.

[1]  José Luis Vicedo González,et al.  Addressing ontology-based question answering with collections of user queries , 2009, Inf. Process. Manag..

[2]  Zongmin Ma,et al.  Answering approximate queries over autonomous web databases , 2009, WWW '09.

[3]  Miltiadis D. Lytras,et al.  AQUA: A Closed-Domain Question Answering System , 2010, Inf. Syst. Manag..

[4]  Subbarao Kambhampati,et al.  Answering Imprecise Queries over Autonomous Web Databases , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[5]  Sanda M. Harabagiu,et al.  Performance Issues and Error Analysis in an Open-Domain Question Answering System , 2002, ACL.

[6]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[7]  Ben Allison An Improved Hierarchical Bayesian Model of Language for Document Classification , 2008, COLING.

[8]  Manuel Palomar,et al.  A knowledge based method for the medical question answering problem , 2007, Comput. Biol. Medicine.

[9]  Maria Soledad Pera,et al.  Web-based closed-domain data extraction on online advertisements , 2013, Inf. Syst..

[10]  Yiu-Kai Ng,et al.  Using Word Clusters to Detect Similar Web Documents , 2006, KSEM.

[11]  James P. Callan,et al.  Structured retrieval for question answering , 2007, SIGIR.

[12]  Nancy C. M. Ross,et al.  End user searching on the Internet: An analysis of term pair topics submitted to the Excite search engine , 2000, J. Am. Soc. Inf. Sci..

[13]  Mark Levene,et al.  Search Engines: Information Retrieval in Practice , 2011, Comput. J..

[14]  Maria Soledad Pera,et al.  A sophisticated library search strategy using folksonomies and similarity matching , 2009, J. Assoc. Inf. Sci. Technol..

[15]  S. Sudarshan,et al.  Ordering the attributes of query results , 2006, SIGMOD Conference.

[16]  PalomarManuel,et al.  A knowledge based method for the medical question answering problem , 2007 .

[17]  Zhen-Yu Wang,et al.  A semantic pattern for restricted domain Chinese question Answering , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[18]  Werner Kießling,et al.  Foundations of Preferences in Database Systems , 2002, VLDB.

[19]  D. S. Wang A Domain-Specific Question Answering System Based on Ontology and Question Templates , 2010, 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[20]  Kristian J. Hammond,et al.  Question Answering from Frequently Asked Question Files: Experiences with the FAQ FINDER System , 1997, AI Mag..

[21]  Young-In Song,et al.  A Practical QA System in Restricted Domains , 2004 .

[22]  Diego Molla Aliod,et al.  Indexing on Semantic Roles for Question Answering , 2008, COLING 2008.