![](bg8.png)
other approaches (Yang and Chua, 2004a; Yang and
Chua, 2004b), which download and process more
than 1000 full web documents, or submit more than
20 queries to different search engines, finishing with
an F
1
score of .464 ∼ .469 on TREC 2003. Our strat-
egy can strengthen their strategy, specially their clas-
sification and clustering of full documents.
In contrast to the observations in TREC 2001
(Voorhees, 2001), duplicate answers have a consid-
erable impact on the performance, because answers
are taken from many different sources. One singular
case is the several spellings and misspellings of an an-
swer. For instance,
ListWebQA
retrieved three differ-
ent spellings/misspellings for the Chuck Berry’s song
“Maybelline” (also found as “Maybellene” and “May-
beline”). Additionally, inexact or incomplete answers
also have an impact on the performance. For exam-
ple, John Updike’s novel “The Poorhouse Fair” was
also found as “Poorhouse Fair”.
5 CONCLUSIONS AND FUTURE
WORK
This paper presented
ListWebQA
, a question answer-
ing system which aimed specially at extracting an-
swers to list questions from web snippets. Our results
indicate that it is feasible to discover answers in web
snippets. We envisage that these answers will help to
select the most promising documents, and afterwards,
detecting the portions where these answers are.
Additionally, we envision that dependency trees
can be used to increase the accuracy of the recognition
of answer candidates, and extra search queries can be
formulated in order to boost the recall of answers in
web snippets. For this last purpose, we deem that
Google n-grams and on-line encyclopaedias would be
tremendously useful.
ACKNOWLEDGEMENTS
This work was partially supported by a research grant
from the German Federal Ministry of Education, Sci-
ence, Research and Technology (BMBF) to the DFKI
project
HyLaP
(FKZ: 01 IW F02) and the EC-funded
project QALL-ME.
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