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Utilizing linguistic analysis in multiple source search engines
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer and Information Science.
2011 (English)MasteroppgaveStudent thesis
Abstract [en]

Modern search engines have several data sources available to users, e.g. News search, Image search and Video search. When a user enters a query in a search engine, it is up to the user to choose a different source than the normal web search. On average, a user will only consider the first few occurrences in a search result and do so in a few seconds. It would therefore be beneficial to the user experience if the user did not have to limit the sources manually to refine a search. This project will evaluate different machine learning methods to classify relevant sources to a query. The goal of this is having an automated learning system that takes some labeled input and uses this to help inform or direct the user to the relevant source. The project will take advantage of a Yahoo! product; Yahoo! Query Linguist Analysis Service (abbreviated QLAS from now on and through the document). The goal is to incorporate semantic data from QLAS into the learning system. This should augment the amount of information available to the learning system, and improve its performance. It is not clear how this semantic data could be combined with the training data and incorporated in the learning system. A substantial part of the project will be to explore this. This project was done in cooperation with Yahoo! Technologies Norway AS (YTN). YTN develops Vespa, a search engine platform that has the possibility to search from multiple sources. YTN is interested in researching the field of learning source relevance to improve the search experience in Yahoo services. YTN is also interested in researching ways data from QLAS could be used by Vespa to enable source relevance classification when Vespa is used in a multiple-index setup.

Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2011. , 114 p.
Keyword [no]
ntnudaim:5772, MIT informatikk, Kunstig intelligens og læring
URN: urn:nbn:no:ntnu:diva-14468Local ID: ntnudaim:5772OAI: diva2:454079
Available from: 2011-11-04 Created: 2011-11-04

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