Author:
Yiu-Kai Ng
Affiliation:
3361 TMCB, Computer Science Department, Brigham Young University, Provo, Utah, U.S.A.
Keyword(s):
Query Processing, Databases, Classification, Data Extraction.
Abstract:
Many commercial websites, such as Target.com, which aspire to increase client’s transactions and thus profits, offer users easy-to-use pull-down menus and/or keyword searching tools to locate advertisements (ads for short) posted at their sites. These websites, however, cannot handle natural language queries, which are formulated for specific information needs and can only be processed properly by natural language query processing (NLQP) systems. We have developed a novel NLQP system, denoted AdProc, which retrieves database records that match information specified in ads queries on multiple ads domains. AdProc relies on an underlying database (DB), which contains pre-processed (ads) records that provides the source of answers to users’ queries. AdProc automates the process of populating a DB using online ads and answering user queries on multiple ads domains. Experimental results using ads queries collected through Facebook on a dataset of online ads extracted from Craigslist.org an
d Coupons.com show that AdProc is highly effective in (i) classifying online ads, (ii) labeling, extracting, and populating data from ads in natural language into an underlying database D, (iii) assigning ads queries into their corresponding domains to be processed, and (iv) retrieving records in D that satisfy the users’ information needs.
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