| Project/Area Number |
14J09896
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Research Field |
Web informatics, Service informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
KRISTIANTO GIOVANNIYOKO 東京大学, 情報理工学系研究科, 特別研究員(DC1)
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| Project Period (FY) |
2014-04-25 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 2016: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2015: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2014: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Mathematical knowledge / Math formulae search / Mathematical expressions / MathML indexing / Dependency graph / Math entity linking / Mathematical Knowledge / Dependency relationships / Math search system / Learning-to-rank / Unification / Description |
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| Outline of Annual Research Achievements |
The objective of this research is to design a mathematical information access (MIA) system, that is a system that allows people to effectively access and process large amounts of mathematical information. We propose a math information retrieval (MIR) module to allow effective search for math information. Then, we design a math entity linking (MEL) module for document browsing. This latter module provides information about math expressions contained in each document by linking these math expressions to their corresponding articles in Wikipedia.
In this year, what we have achieved are:
(1) The evaluation of our MIR module in the NTCIR-12 MathIR task. The results showed that our system finished at the first place. The overall results were published in the 12th NTCIR conference. In addition, the detail of the key component of our MIR module was published in the Information Retrieval Journal.
(2) The initial design of MEL module. Our initial MEL module was based on the MIR. The evaluation showed that the initial module did not perform well. This result was published in the 18th ICADL.
(3) The development of a supervised-learning approach for MEL. We constructed a dataset for this task, then proposed a method for measuring the importance of a given math expressions in its containing document, and finally implemented several features (i.e. math and text similarity, math importance, and math prominence). It was shown that the latter MEL module achieved a precision of 83.40%, compared with 6.22% for our initial module. This work was published in the SWM at WSDM 2017 conference.
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| Research Progress Status |
28年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
28年度が最終年度であるため、記入しない。
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