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1 I 1 Web Tani Shinichi Taniguchi / recall precision 1 2 CHHY HUY Spring / No.399

2 Fig , II 20% 2002 TLO 3 Ohmura 2003 TLO TLO COC 4 Fig.1 Web 検 索 回 数 年 度 別 アクセス 数 推 移 30,000 25,000 20,000 15,000 10,000 5, 年 度 Fig.1 III 3.1. Tani craler 9 2 Web document set DD= d1, d2, dn 3 Technology Licensing Organization TLO 38 TLO COC index.htm 101

3 2 D D indexing term set :T T= t1, t2,, tm 3 D T ti idf inverse document frequency ti idf n/nin ni ti 4 D ij termdocument matrix 1 ij djti ti ti tf term frequency 3 ti idf tf-idf Kita D=[ d d d ] = q 2 q = 1 2 q1 q2 qm n m 2 qi qi ti 6 D q m n1 n2 nm dj q cos( d, q) = j d j q = d j q 3 7 dj Toku relevance 2 pertinence 3 usefulness Foskett 1972 Kemp 1974 A X Y AX X 4 4 m i=1 ij qi 2 m ij m i=1 i=1 2 qi Spring / No.399

4 A completeness 2 accuracy R recall P precisiontable.1 Table.1 x y z A R 5 R= + x B P 6 P= + y 5 6 D dj qi test collection 3.4. Kishi DNA 103

5 Table.2 3 > x Table.2 ID R P 1 R R R R R R R R R R R R R R R R R R R = / =5, y= /5 Ri recall level :Riprecision level :Pi Table.2 Fig.2 0, tradeoff, tf-idf Spring / No.399

6 Fig.2 精 度 (precision) 再 現 率 - 精 度 グラフ(タンパク 質 で 検 索 ) 再 現 率 (recall) Fig.2 Table Table Table.3 Fig.3 Table.3 ID R P 1 R R R R R R R R R R R R R R R R R R R R R R R R R R R 精 度 (precision) 再 現 率 - 精 度 グラフ (シソーラスを 利 用 してタンパク 質 で 検 索 ) Fig.3 Table.3 再 現 率 (recall) 105

7 2 1 macro average R P 78 Q i, xi, yi Table.1 1 Q R = i Q i = 1 i + x i 1 Q P = i = 7 8 2micro average R = P = Q 1 Table.1 R P i i + y i Q = i 1 i ( i+ x i 1 i ) Q = Q = i 1 i ( + y ) Q = i 1 i i IV Table.3 1R48 q Table.3 Fig Spring / No.399

8 Fig 精 度 (precision) 再 現 率 (recall) 4.2. R.Mandana, T.Tokunaga Cranfield INSPEC 7 WordNet Manda 1998 Toku 1999 再 現 率 - 精 度 グラフ (タンパク 質 とシソーラスで 検 索 ) Fig distribution hypothesis 1 Hind Pere Naka Toku Manda TTM Tiny Text Miner Matsu

9 R Ishi 2008 Ishi 2012 Step1. CSV N_Bio.csv Step2. N_Bio.csv TTM ttm3 ttm5 MeCab 6 Step3. ttm3 ttm5 N_Bio.csv 2,832 Step4. Step2 Step3 ttm3,ttm5 Step5. N_bio_ttm5.csv 2 2 R igraph plot Fruchterman-Reingold layout 7 Fig.5 Fig.5 Fig.7 Fig.5 Table.4 ID R P 1 R R R R R R R R R R R R R R R R R R R R R R R R R R R R R doc/index.html Spring / No.399

10 精 度 (precision) Fig.6 Table.4Fig.6 再 現 率 - 精 度 グラフ (タンパク 質 と 拡 張 シソーラスで 検 索 ) 再 現 率 (recall) Table.2 Fig tf-idf D dj D dj V Table

11 or Fig Spring / No.399

12 Foskett 1972 Foskett, D. J.(1972) / A note on the concept of relevance, Information Storage and Retrieval 8 2, pp.77-78, 1972 Hind 1990 Hindle,D. (1990) / Noun Classification from predicate-argument structures In Proceedings of the 28 th Annual Meeting of the Association for Computational Linguistics, pp Ishi R pp Ishi pp Kemp 1974 Kemp,D.A.(1974) / Relevance, pertinence and information system development Information Storage and Retrieval 10 2 pp Salton 1975 Salton,G., Wong,A. and Yang,CS.(1975) / A Vector Space Model for automatic indexing Communication of the ACM, Vol.18, No.11, pp Tani pp Toku 1997 Tokunaga,T., Fujii,A., Iayama,M., Sakurai,N. and Tanaka,H.(1997) / Extending a thesaurus by classifying ords In Proceedings of the ACL/EACL Workshop on Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications, pp Toku pp Kishi pp Kita pp Manda 1998 Mandara,R., Tokunaga, T. & Tanaka,H.(1998) / The use of WordNet in information retrieval Proceedings of Coling-ACL 98 orkshop Usage of WordNet in Natural Language Processing Systems, pp Matsu pp Naka , CL-25, 1981 Ohmura Pere 1993 Pereira,F., Tishby,N. & Lee,L.(1993) / Distributional clustering of English ords Proceeding of the 31 st Annual meeting of the Association for Computational Linguistics, pp

13 Improvements to a Shiga-Based Academic Research Retrieval System through the Application of Text Mining Shinichi Taniguchi For this paper, the performance of an academic research retrieval system called Chie- Navi as quantitatively evaluated for its performance based on Recall and Precision. Here, Chie-Navi commissioned by Shiga Prefecture in 2004 aims to navigate academic researchers in Shiga to coordinators ho ork at industrial support organizations. Recall is the fraction of relevant documents that are retrieved to the query, hile Precision is the fraction of the retrieved documents that are relevant to the user's information need. Generally, Relationship beteen Recall and Precision are a trade-off. Therefore, "Chie-Navi" gives priority to Recall in order to provide appropriate researchers to the coordinators ithout omission. To improve the performance of Recall, a general-purpose thesaurus is applied to "Chie-Navi". On the other hand, a number of access by general users has been increasing recently. So it is necessary to improve the performance of Precision. To achieve this purpose, performance of a general-purpose thesaurus has to be evaluated quantitatively and applied a method for improving Precision. To do so, first, a test collection as created to determine the correctness to the query. Second, the performance based on Recall and Precision as evaluated in the case of utilizing the thesaurus or not. As a result, Recall as improved ell, but Precision as not improved so much. Generally, dependence on the ords in a different group is not contained in a thesaurus. In other ords, a thesaurus is a reference ork that lists ords grouped together according to similarity of meaning. Hoever, it is said a combination of that dependence is effective to improve a general-purpose thesaurus. So, the author applied text mining techniques in order to discover dependence on the ords, and added dependent ords in a different group, such as Protein and Cancer, to the thesaurus. Through the feasibility test, it turned out that this method as able to effectively improve not only Recall but also Precision. 112 THE HIKONE RONSO 2014 Spring / No.399

14 Improvements to a Shiga-Based Academic Research Retrieval System through the Application of Text Mining Shinichi Taniguchi 113

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