The heterogeneous Web exacerbates IR problems and short
user queries make them worse. The contents of web documents
are not enough to find good answer documents. Link
information and URL information compensates for the insu
\#ciencies of content information. However, static combination
of multiple evidences may lower the retrieval performance.
We need di\#erent strategies to find target documents
according to a query type. We can classify user
queries as three categories, the topic relevance...
(private-note)They are building an automated classification approach in order to classify queries into the 3 categories: homepage finding task, service finding and topic relevancy.
They are using POS and anchor text information.
%0 Generic
%1 citeulike:1927582
%A Kang, I.
%A Kim, G.
%D 2003
%K analysis, classification, log, pos, query, retrieval, web
%T Query type classification for web document retrieval
%U http://citeseer.ist.psu.edu/kang03query.html
%X The heterogeneous Web exacerbates IR problems and short
user queries make them worse. The contents of web documents
are not enough to find good answer documents. Link
information and URL information compensates for the insu
\#ciencies of content information. However, static combination
of multiple evidences may lower the retrieval performance.
We need di\#erent strategies to find target documents
according to a query type. We can classify user
queries as three categories, the topic relevance...
@misc{citeulike:1927582,
abstract = {The heterogeneous Web exacerbates IR problems and short
user queries make them worse. The contents of web documents
are not enough to find good answer documents. Link
information and URL information compensates for the insu
\#ciencies of content information. However, static combination
of multiple evidences may lower the retrieval performance.
We need di\#erent strategies to find target documents
according to a query type. We can classify user
queries as three categories, the topic relevance...},
added-at = {2008-06-17T16:01:02.000+0200},
author = {Kang, I. and Kim, G.},
biburl = {https://www.bibsonomy.org/bibtex/2b64d20e142df29f6fd5e6b5df0065c59/pprett},
citeulike-article-id = {1927582},
comment = {(private-note)They are building an automated classification approach in order to classify queries into the 3 categories: homepage finding task, service finding and topic relevancy.
They are using POS and anchor text information.},
interhash = {b67664b4eeaed4993906caa779fc363a},
intrahash = {b64d20e142df29f6fd5e6b5df0065c59},
keywords = {analysis, classification, log, pos, query, retrieval, web},
posted-at = {2007-11-16 18:55:06},
priority = {2},
timestamp = {2008-06-17T16:01:46.000+0200},
title = {Query type classification for web document retrieval},
url = {http://citeseer.ist.psu.edu/kang03query.html},
year = 2003
}