An Automated Research Paper Classification Method for the IPC system with Concept Basefor Japanese Subtask at NTCIR-7 Patent Mining Task
Takanori Shimano and Takashi Yukawa(Nagaoka University of Technology)

Background and Motivation
There is a problem of classification in that patentdocuments differ from research papers respect todocument characteristic that is as follows:•Term•Document structure

Concept Based Vector Space Model
To solve the problem of the term difference, aclassification method using the CBVSM isproposed.
w1z
In a simple Vector SpaceModel, vectors of words
do not point in the samedirection even if they
are synonym each other.

A Classification Method using the Concept BasedVector Space Model
Target ResearchPaperpP̂

Simple Vector
Space Modelw1

w2

In a Concept Based
Vector Space Model,xvectors of semanticallysimilar words point inythe similar direction.
Concept Based Vector
Space Model

Simple Vector Space Model

SVDqq
d1

Concept
Base

Transform by the Concept Basexz

Co-occurMatrix

Weight by the TFIDF

w2yPatents

Concept Based
Vector Space Modelqqd̂1Classification• Compute the score of the research paper for each class• Rank the classes based on the score

ˆˆp
dscore(pˆ , C )   sim(pˆ , dˆ )  ˆˆ|p||d|dCdCThe AP value of the CBVSMbased method is higher thanthe simple VSM-based
method in 33% of all topics.

Evaluation Results

MAP

Mean Average Precisionusing the simple
VSM-based method

5045
40
35
30
2520
15
1050

Comparison of AP values

using theCBVSM-based
methodTopic-ID
300
301
302
303
…
MAP

Run-ID

Simple VSM-based
method
0.05560.01640.11111.0000…
0.2963

CBVSM-based
method0.1667
0.08331.00000.0139…
0.2388On the CBVSM-based method, theoccurrence of the words w7 and w21
contributes to the concept c341 andapproximates the vector of the correct
patent to the vector of the topic.

Discussion
The word that has a too large TF
value decreases the MAP value
A Document Vector of Patent #95070321
The TF value of theword w6 is too large,
however, the word
is not useful for
retrieval of patentdocuments in thetopic.

Wordw1…
w6…
w20Conclusions

TF
7163
1

DFTFIDF
219657 0.0552
770947 0.7026
360676 0.0065

Why the AP value of the CBVSM-based method is higher in 33% of allDocument Vectors on the Simple VSM-based Method Document Vectors on the CBVSM-based Method
topics?
In classification of
the topic #302, theAP value of theCBVSM-based
method is higher
than the simple
VSM-based method.

Word

Topic

d1

d2

Concept

Topic

d1d2

w7

0.14150.08950.0127c341-0.2516-0.1826-0.1070w100.4772

0.57440.2741c346

0.08670.08670.1272w210.0000

0.16000.0000c359-0.2142-0.2142-0.1568

0.83970.8367…

…
simThe word w21 does not increase
the score of similarity on thesimple VSM-based method.

0.47600.5085The patent document
d1 includes the samecontent as the topic
#302.

sim
The patent document d2
includes a content thatdiffers from the topic#302.

•The CBVSM-based classification method is proposed for the research paper classification.
•The MAP value of the method is lower than the simple VSM-based method.
•However, in 33% of all topics, the AP value of the method is higher than the simple VSM-based method.
•In the future, we intend to investigate two areas of concern:•Address the problem of a large TF value by setting a ceiling value.
•Improve the accuracy of the classification using a combination of the CBVSM and the simple VSM.