Entry Higashinaka:2008:AAC from talip.bib
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BibTeX entry
@Article{Higashinaka:2008:AAC,
author = "Ryuichiro Higashinaka and Hideki Isozaki",
title = "Automatically Acquiring Causal Expression Patterns
from Relation-annotated Corpora to Improve Question
Answering for why-Questions",
journal = j-TALIP,
volume = "7",
number = "2",
pages = "6:1--6:??",
month = jun,
year = "2008",
CODEN = "????",
DOI = "https://doi.org/10.1145/1362782.1362785",
ISSN = "1530-0226 (print), 1558-3430 (electronic)",
ISSN-L = "1530-0226",
bibdate = "Mon Jun 16 17:12:23 MDT 2008",
bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/talip.bib",
abstract = "This article describes our approach for answering
why-questions that we initially introduced at NTCIR-6
QAC-4. The approach automatically acquires causal
expression patterns from relation-annotated corpora by
abstracting text spans annotated with a causal relation
and by mining syntactic patterns that are useful for
distinguishing sentences annotated with a causal
relation from those annotated with other relations. We
use these automatically acquired causal expression
patterns to create features to represent answer
candidates, and use these features together with other
possible features related to causality to train an
answer candidate ranker that maximizes the QA
performance with regards to the corpus of why-questions
and answers. NAZEQA, a Japanese why-QA system based on
our approach, clearly outperforms baselines with a Mean
Reciprocal Rank (top-5) of 0.223 when sentences are
used as answers and with a MRR (top-5) of 0.326 when
paragraphs are used as answers, making it presumably
the best-performing fully implemented why-QA system.
Experimental results also verified the usefulness of
the automatically acquired causal expression
patterns.",
acknowledgement = ack-nhfb,
articleno = "6",
fjournal = "ACM Transactions on Asian Language Information
Processing",
journal-URL = "http://portal.acm.org/browse_dl.cfm?&idx=J820",
keywords = "causal expression; pattern mining; question answering;
relation-annotated corpus",
}
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9(2)7,
10(3)14,
10(3)15,
11(1)3,
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11(4)16,
11(4)18,
12(1)3,
13(1)2,
13(1)3,
13(1)4
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5(2)146,
5(2)165,
7(4)11,
8(1)4,
11(2)5,
11(3)11
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5(2)146,
5(2)165,
6(2)6,
6(2)7,
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9(2)5,
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12(3)11,
12(4)14,
12(4)16,
13(1)1,
13(1)4,
13(2)6,
13(2)7,
13(2)9,
13(3)11,
13(3)12,
13(3)14
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1(3)173,
3(2)146,
4(3)321,
4(4)377,
5(2)121,
5(2)146,
5(2)165,
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12(4)14,
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8(1)4,
8(4)15,
9(2)6,
10(3)12,
10(3)15,
10(4)17,
11(3)8,
11(4)14,
11(4)15,
11(4)18,
12(4)17,
13(2)9
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1(1)34,
1(2)159,
3(3)190,
3(4)215,
4(1)38,
4(2)135,
4(4)435,
5(1)1,
5(2)165,
6(1)z-3,
6(3)10,
6(4)2,
7(3)8,
7(3)9,
8(1)4,
8(3)11,
8(4)14,
8(4)16,
8(4)18,
9(1)1,
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9(4)15,
10(3)14,
11(1)2,
11(2)4,
11(2)5,
11(4)13,
11(4)14,
11(4)15,
11(4)16,
11(4)17,
11(4)18,
12(1)2,
12(1)3,
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12(3)11,
12(4)15,
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13(1)4,
13(2)7,
13(2)8,
13(2)9,
13(2)10,
13(3)14
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6(3)11,
7(3)8,
8(3)12,
9(3)11,
10(3)12,
12(3)9
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8(3)10,
12(3)9
- use,
4(2)159,
5(2)89,
5(2)146,
6(2)8,
6(3)11,
7(3)9,
7(4)11,
7(4)12,
8(1)3,
8(2)9,
8(3)10,
8(3)11,
9(1)1,
9(1)3,
9(3)11,
10(1)3,
10(1)4,
11(1)1,
11(2)6,
11(2)7,
11(3)8,
11(3)10,
11(4)14,
11(4)18,
12(1)1,
12(2)6,
12(3)9,
12(3)10,
13(2)6,
13(2)9,
13(2)10,
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- used,
5(2)89,
5(2)146,
7(1)3,
7(2)7,
7(3)9,
7(4)12,
7(4)13,
8(3)10,
8(4)17,
9(1)1,
9(1)3,
9(2)6,
9(3)10,
10(1)2,
10(1)6,
10(2)7,
10(2)8,
10(3)12,
10(3)13,
10(4)20,
11(1)2,
11(1)3,
11(3)10,
11(4)13,
11(4)14,
12(2)5,
12(3)9,
12(3)11,
12(3)12,
13(2)6,
13(3)11
- useful,
4(3)243,
5(2)146,
6(4)1,
8(4)19,
9(3)12,
10(4)20,
12(4)16,
13(3)13
- usefulness,
3(4)227,
10(2)8,
13(2)6
- verified,
8(1)2,
9(2)5
- when,
5(2)89,
6(3)9,
6(3)11,
7(4)11,
7(4)13,
8(1)3,
8(4)16,
8(4)17,
9(1)3,
9(2)5,
9(3)11,
9(3)12,
9(4)13,
11(2)4,
12(1)2,
12(4)14,
13(2)8,
13(3)12