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1 14 Application of Automatic Text Summarization for Question Answering System
2 Prassie Posum Prassie Prassie i
3 Abstract Application of Automatic Text Summarization for Question Answering System Tomohiko KAWACHI Natural language processing is one of the effective techniques in information retrieval. Question answering is a technique of information retrieval with natural language like to ask a person, and answers arbitrary questions written in natural language. This paper proposes applying an automatic text summarization tool, called Posum, to a question answering system, called Prassie, in order to improve Prassie. First, the range of answer extraction of the Prassie was improved. Second, a newspaper article for the answer extracting was summarized with the improved way to calculate importance. As a result, the Prassie could extract right answers from a sentence not including index words for several questions, the answers of which was wrong in case of a system not making use of Posum, and the performance of extract answers was improved slightly. Therefore, it was confirmed that the automatic text summarization operate effectively to question answering. key words natural language processing, question answering, automatic text summarization ii
4 1 1 2 QAC 3 3 Prassie ChaSen( ) Posum term frequency Prassie Version0.4.1-saku iii
5 Version0.10.0, Version A 30 B 32 C 34 iv
6 2.1 Prassie Prassie Prassie Prassie (Version ) Prassie (Version ) ChaSen Version Version0.4.1-saku Version Version v
7 5.1 Version Version0.4.1-saku Version0.10.0, Version Version0.10.0(3 ) Version0.10.0(6 ) C C vi
8 1 Prassie [1] Posum [2] Prassie Prassie Prassie [3] Prassie 1
9 2
10 2 QAC Question Answering Challenge (QAC) [6] QAC QAC NTCIR Workshop 3 [4][5] Prassie 2.1 Prassie NTCIR Workshop 3 QAC Prassie 2.1 Prassie QAC QAC ( ) Prassie Prassie 2.2 QAC 3
11 Prassie 2.2 Prassie 4
12 3 Prassie Prassie [1] Prassie 3.1 Prassie Prassie Version Prassie 5
13 N = (N = MAX) N No No N N - Yes Yes Yes No N No Yes 3.2 Prassie (Version ) 6
14 3.1 ChaSen( ) N No N Yes Yes N N + No No N > Yes 3.3 Prassie (Version ) 3.1 ChaSen( ) ChaSen [7] Prassie, Posum ChaSen ChaSen 3.4 7
15 3.2 ChaSen ChaSen 3.2 ChaSen 3.5,,, 3.5 8
16 Version
17
18 4 Posum Posum [2] Posum Posum 4.1 Posum ( ) 4.2 term frequency term frequency( tf ) tf tf A A ChaSen A 11
19 Posum tf tf 12
20 5 Prassie(Version ) Posum Posum Prassie QAC 1. Prassie 2. Prassie 3. Prassie ChaSen Prassie Prassie(Version ) 13
21 Posum Prassie Prassie Posum Version Prassie Version Version Version Version Prassie 14
22 5.2 Prassie 5.1 Version Prassie 5.2 Prassie Version0.4.1-saku Prassie Posum 15
23 5.2 Prassie 5.1 Version Version Version Version0.4.1-saku
24 5.2 Prassie Yes N 1 No N Yes N N + No No N > Yes 5.2 Version0.4.1-saku Version Version0.4.1 Version Version0.4.1-saku
25 5.2 Prassie 5.2 Version0.4.1-saku Version Version Version Version
26 5.2 Prassie Version0.10.0, Version Version0.10.0, Version Version Version Version Version Version Version
27 5.2 Prassie Yes N 1 No N Yes N N + No No N > Yes 5.3 Version
28 5.3 Yes N 1 No N Yes N N + No No N > Yes 5.4 Version
29 Version0.10.0, Version Version Version Version Version
30 Version Version ( ) tf tf saku most kakko least 5.4 Version0.10.0(3 ) saku most kakko least Version Version
31 Version0.10.0(6 ) saku most kakko least Version Version Version Version Version Version
32 5.3 25
33 6 Prassie Posum Prassie Version Posum Prassie 26
34 27
35 28
36 [1] Takayuki TOMOIKE, Tomohiko KAWACHI, Ruck THAWONMAS, Akio SAKAMOTO, Article Retrieval and Answer Extraction Exploiting Characteristics in Newspaper Articles for the QAC Task2, Working Notes of the Third NTCIR Workshop Meeting Part IV: Question Answering Challenge, pp , [2] Posum Home Page : [3] NewsInEssence Home Page : [4] NTCIR Home Page : [5] NTCIR Workshop 3 Home Page : [6] QAC Home Page : [7] ChaSen Home Page : 29
37 A 42 QAC [QAC ] QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : 30
38 QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : QAC : 31
39 B 42 QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC
40 QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC
41 C Version Prassie Version Version Prassie Version C.1 Version Version QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC
42 C.2 Version Version QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC QAC
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