Paper Info
Title
A math-aware search engine for math question answering system
Abstract
We propose a math-aware search engine that is capable of handling both textual keywords as well as mathematical expressions. Our math feature extraction and representation framework captures the semantics of math expressions via a Finite State Machine model. We adapt the passive aggressive online learning binary classifier as the ranking model. We benchmarked our approach against three classical information retrieval (IR) strategies on math documents crawled from Math Overflow, a well-known online math question answering system. Experimental results show that our proposed approach can perform better than other methods by more than 9%.
Year
DOI
Venue
2012
10.1145/2396761.2396854
CIKM
Keywords
Field
DocType
math document,math overflow,math feature extraction,math-aware search engine,well-known online math question,passive aggressive online,answering system,math question answering system,finite state machine model,ranking model,math expression,learning to rank
Learning to rank,Question answering,Expression (mathematics),Ranking,Binary classification,Information retrieval,Computer science,Finite-state machine,Theoretical computer science,Feature extraction,Semantics
Conference
Citations 
PageRank 
References 
17
0.80
21
Authors
3
Name
Order
Citations
PageRank
Tam T. Nguyen1786.79
Kuiyu Chang291760.50
Siu Cheung Hui3110686.71