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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (1): 131-138    DOI: 10.11925/infotech.2096-3467.2019.0943
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Retrieving Scientific Documents with Formula Description Structure and Word Embedding
Xinyu Zai,Xuedong Tian()
School of Cyber Security and Computer, Hebei University, Baoding 071002, China
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Abstract  

[Objective] This study proposes a scientific document retrieval method combining formula match and text ranking, which address the challenges from mathematical expressions.[Methods] First, we used the analysis algorithm for formula description structure to study the mathematical expressions. Then, we acquired formula structure information, and retrieved technical documents based on mathematical expressions. Meanwhile, we obtained the inquiry keywords and document word vectors with the help of word embedding model. Finally, we ranked the documents based on the similarity between the two word vectors[Results] The recall and precision scores of our new model were 0.77 and 0.63, which were 24.2% and 23.5% higher than those of the traditional scientific document retrieval methods.[Limitations] Our method only focuses on expressions in LaTeX format.[Conclusions] The proposed model combining formula and document keywords improves the performance of scitific document retrieval.

Key wordsTechnical Document Retrieval      Formula Description Structure      Word Embedding     
Received: 13 August 2019      Published: 14 March 2020
ZTFLH:  TP311  
Corresponding Authors: Xuedong Tian     E-mail: xuedong_tian@126.com

Cite this article:

Xinyu Zai,Xuedong Tian. Retrieving Scientific Documents with Formula Description Structure and Word Embedding. Data Analysis and Knowledge Discovery, 2020, 4(1): 131-138.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0943     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I1/131

CBOW Model
查询表达式 LaTeX结构 FDS结构
2q 2^{q} ^\1
a×b a \times b \times\0,
ab \frac{a}{b} frac\0
-b±b2-4ac2a \frac{-b ±√({b^{2} -4 a c} )}{2 a} \frac\0,-\1,\pm\1,\sqrt\1,^\3,-\2,
1σ2πe-x-μ22σ2 \frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-\mu)^{2}}
{2 \sigma^{2}}}
\frac\0,\sqrt\1,^\1,-\1,\frac\1,(\2,-\2,)\2,^\3,^\3,
Partial Expression Parsing Results
EXPID EXP FileName(html)
57113 pxμσ=1σ2πe- x-μ22σ2 Computer stereo vision
127297 PGZ=1σ2πe- x-μ22σ2 Gaussian noise
206443 px|μσ=1σ2πe- x-μ22σ2 Maximum entropy probability distribution
232616 fx|μ,σ=1σ2πe- x-μ22σ2 Normal distribution
79135 gx=12πσ2e- x-μ22σ2 Differential entropy
Partial Search Results of Expression
Keyword WordScore
folded normal distribution 7.37
folded distribution 5.03
normal distribution 4.78
random variable 4.33
differential equations 4.02
Keyword Group Crawl Results
序号 文档(html) 相似度
1 Folded normal distribution 0.93
2 Normal gamma distribution 0.86
3 Gaussian distribution 0.80
4 Exponential family 0.75
5 Stochastic simulation 0.74
6 Logit normal distribution 0.73
7 Normal distribution 0.72
8 Kernel (statistics) 0.68
9 Distributed random 0.67
10 Slice sampling 0.66
Document Sorting Top-10 Results
系统 公式 文档(html)
Search
OnMath
p(k)=λkk!e-λ Variance
fk;λ=Pr(X=k)=λke-λk! Poisson distribution
p(d)=λdd!e-λ Long tail traffic
pn=i=1T1nMinie-Mi Constellation model
Q(ψn)(x,p)=x2+p2n!e-x2+p2π Quantum harmonic oscillator
本文系统 p(k)=λkk!e-λ Variance
fk;λ=Pr(X=k)=λke-λk! Poisson distribution
p(N=k)=λkk!e-n Poisson games
Pn(t)=tkn!e-t Poisson wavelet
λkk!e-λ=5kk!e-5 Poisson limit theorem
Top-5 Search Results for Both Systems
序号 公式 关键字 序号 公式 关键字
1 yt fractional 6 limn1+1nn limit theorem
2 2q exponential 7 a2+b2=c2 pythagorean theorem
3 sinθ sine function 8 λkk!e-λ poisson
4 cosx cosine function 9 -b±b2-4ac2a quadratic formula
5 a radical expression 10 1σ2πe- x-μ22σ2 normal distribution
Document List
Comparison of Similarity Between Our Method and SearchOnMath
Retrieval Recall and Precision
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