東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Recommending Answers to Math Questions Using KL-Divergence and the Approximate XML Tree Matching Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Recommending Answers to Math Questions Using KL-Divergence and the Approximate XML Tree Matching Approach./
Author:	Gao, Siqi.
Description:	1 online resource (62 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-04.
Contained By:	Masters Abstracts International85-04.
Subject:	Calculus. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614047click for full text (PQDT)
ISBN:	9798380468909

Recommending Answers to Math Questions Using KL-Divergence and the Approximate XML Tree Matching Approach.
Gao, Siqi.

Recommending Answers to Math Questions Using KL-Divergence and the Approximate XML Tree Matching Approach. - 1 online resource (62 pages)

Source: Masters Abstracts International, Volume: 85-04.

Thesis (M.Sc.)--Brigham Young University, 2023.

Includes bibliographical references

Mathematics is the science and study of quality, structure, space, and change. It seeks out patterns, formulates new conjectures, and establishes the truth by rigorous deduction from appropriately chosen axioms and definitions. The study of mathematics makes a person better at solving problems. It gives someone skills that (s)he can use across other subjects and apply in many different job roles. In the modern world, builders use mathematics every day to do their work, since construction workers add, subtract, divide, multiply, and work with fractions. It is obvious that mathematics is a major contributor to many areas of study. For this reason, retrieving, ranking, and recommending Math answers, which is an application of Math information retrieval (IR), deserves attention and recognition, since a reliable recommender system helps users find the relevant answers to Math questions and benefits all Math learners whenever they need help solve a Math problem, regardless of the time and place. Such a recommender system can enhance the learning experience and enrich the knowledge in Math of its users. We have developed M aRec, a recommender system that retrieves and ranks Math answers based on their textual content and embedded formulas in answering a Math question. M aRec (i) applies KL-divergence to rank the textual content of a potential answer A with respect to the textual content of a Math question Q, and (ii) together with the representation of the Math formulas in Q and A as XML trees determines their subtree matching scores in ranking A as an answer to Q. The design of M aRecis simple, since it does not require the training and test process mandated by machine learning-based Math IR systems, which is tedious to set up and time consuming to train the models. Conducted empirical studies show that M aRec significantly outperforms (i) three existing state-of-the-art MathIR systems based on an offline evaluation, and (ii) a top-of-the-line machine learning system based on an online performance analysis.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798380468909Subjects--Topical Terms:

517463
Calculus.
Index Terms--Genre/Form:

542853
Electronic books.

Recommending Answers to Math Questions Using KL-Divergence and the Approximate XML Tree Matching Approach.
LDR:03365nmm a2200361K 4500 001 2364617
005 20231130105854.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798380468909
035 $a (MiAaPQ)AAI30614047
035 $a (MiAaPQ)BrighamYoung10975
035 $a AAI30614047
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Gao, Siqi. $3 3705433
245 1 0 $a Recommending Answers to Math Questions Using KL-Divergence and the Approximate XML Tree Matching Approach.
264 0 $c 2023
300 $a 1 online resource (62 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-04.
500 $a Advisor: Ng, Yiu-Kai;Giraud-Carri, Christophe Gerard;Jenkins, Porter Reece.
502 $a Thesis (M.Sc.)--Brigham Young University, 2023.
504 $a Includes bibliographical references
520 $a Mathematics is the science and study of quality, structure, space, and change. It seeks out patterns, formulates new conjectures, and establishes the truth by rigorous deduction from appropriately chosen axioms and definitions. The study of mathematics makes a person better at solving problems. It gives someone skills that (s)he can use across other subjects and apply in many different job roles. In the modern world, builders use mathematics every day to do their work, since construction workers add, subtract, divide, multiply, and work with fractions. It is obvious that mathematics is a major contributor to many areas of study. For this reason, retrieving, ranking, and recommending Math answers, which is an application of Math information retrieval (IR), deserves attention and recognition, since a reliable recommender system helps users find the relevant answers to Math questions and benefits all Math learners whenever they need help solve a Math problem, regardless of the time and place. Such a recommender system can enhance the learning experience and enrich the knowledge in Math of its users. We have developed M aRec, a recommender system that retrieves and ranks Math answers based on their textual content and embedded formulas in answering a Math question. M aRec (i) applies KL-divergence to rank the textual content of a potential answer A with respect to the textual content of a Math question Q, and (ii) together with the representation of the Math formulas in Q and A as XML trees determines their subtree matching scores in ranking A as an answer to Q. The design of M aRecis simple, since it does not require the training and test process mandated by machine learning-based Math IR systems, which is tedious to set up and time consuming to train the models. Conducted empirical studies show that M aRec significantly outperforms (i) three existing state-of-the-art MathIR systems based on an offline evaluation, and (ii) a top-of-the-line machine learning system based on an online performance analysis.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Calculus. $3 517463
650 4 $a Applied mathematics. $3 2122814
650 4 $a Recommender systems. $3 3562220
650 4 $a Probability. $3 518898
650 4 $a Similarity measures. $3 3705434
650 4 $a Performance evaluation. $3 3562292
650 4 $a Keywords. $3 3560140
650 4 $a Probability distribution. $3 3562293
650 4 $a Chatbots. $3 3705435
650 4 $a Information retrieval. $3 566853
650 4 $a Natural language. $3 3562052
650 4 $a Information science. $3 554358
650 4 $a Statistics. $3 517247
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0364
690 $a 0800
690 $a 0723
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Brigham Young University. $3 1017451
773 0 $t Masters Abstracts International $g 85-04.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614047 $z click for full text (PQDT)