Qingtang Liu
ABSTRACT
Mathematical automatic problem solving is a very challenging task in the field of artificial intelligence. The key premise of problem-solving automatically is to understand the problem's meaning. For mathematical word problems with rich semantics, varied forms, and difficult to be understood by machines, this study focuses on solving the difficulty of overlapping entity relations recognition and multiple entity relation extractions across sentences, taking the word problem of the classical probability as the research object, an entity relations extraction method based on sequence annotation is proposed. The BiLSRM-CRF model is used to improve the effect of question comprehension. The experimental study found that, compared with the selected combination of different features based on the CRF model alone, the BiLSTM-CRF model can obtain the effect of the approximate CRF model at a superior cost, and improve the recognition effect of a few relations. Meanwhile, the accuracy of the overall problem understanding also gets improved.
- Bobrow D G. 1964. Natural language input for a computer problem-solving system. J. semantic information processing, 146-226.Google Scholar
- Wenjun Wu. 1977. Elementary geometric determination problem and mechanization proof. J. Chinese Science. 6, 507-516.Google Scholar
- Schoenfeld A H. 2013. Reflections on Problem Solving Theory and Practice. J. Mathematics Enthusiast 10, 1, 9-34.Google ScholarCross Ref
- Fujita A, Kameda A, Kawazoe A, 2014. Overview of Todai Robot Project and Evaluation Framework of its NLP-based Problem Solving. In Proceedings of the Ninth International Conference on Language Resources and Evaluation(LREC'14). Reykjavik, Iceland, 2590-2597.Google Scholar
- Linjing Wu, Chuanyuan Lao, Qingtang Liu, 2019. Stratified Sampling Word Problem Understanding of Elementary Mathematics Based on Dependency Syntax. J. Computer applications and software. 36, 5, 126-132.Google Scholar
- Zhihui Zhang. 2017.Research and Application of Automatic Classification of Elementary Mathematical Problems Based on SVM. PHD Thesis, University of Electronic Science and Technology of China, UESTC.Google Scholar
- Charniak E. 1969. Computer solution of calculus word problems. In Proceedings of the 1st international joint conference on Artificial intelligence. Washington, DC, 303-316.Google Scholar
- Riley, M. S. 1984. Development of children's problem-solving ability in arithmetic. J. The development of mathematical thinking, 153-196.Google Scholar
- Kintsch W, Greeno J G. 1985. Understanding and solving word arithmetic problems. J. Psychological review, 92,1,109-129. https://doi.org/10.1037/0033-295x.92.1.109Google Scholar
- Wong W, Hsu S, Wu S, 2007. LIM-G: Learner-initiating instruction model based on cognitive knowledge for geometry word problem comprehension. J. COMPUTERS & EDUCATION, 48, 4, 582-601.https://doi.org/10.1016/j.compedu.2005.03.009Google ScholarDigital Library
- Kyle Morton, Yanzhen Qu, 2013. A Novel Framework for Math Word Problem Solving. International Journal of Information and Education Technology, Canada Montreal, 88-93.Google Scholar
- Kang B, Kulshreshth A, Laviola Jr J J. 2016. Analyticalink: An interactive learning environment for math word problem solving. In Proceedings of the 21st International Conference on Intelligent User Interfaces. ACM , Sonoma, 419-430. https://doi.org/10.1145/2856767.2856789Google ScholarDigital Library
- Bo Song, Rui-Fu Wang, Xiao-Mei Li. 2020. The Research on the Construction of Primary Mathematics Corpus Based on MATTER Cycle Method. J. International Journal of Information and Education Technology, 10, 4, 304-308.Google ScholarCross Ref
- Bollacker K, Evans C, Paritosh P, 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, British Columbia,1247-1250. https://doi.org/10.1145/1376616.1376746.Google ScholarDigital Library
- Chinchor N, Marsh E. 1998. Muc-7 information extraction task definition. In Proceeding of the seventh message understanding conference (MUC-7). Appendices, 359-367.Google Scholar
- Yangarber R. 1998. NYU: Description of the Proteus/PET System as Used for MUC-7 ST. In Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29-May 1.Google Scholar
- Hui Zeng, Jiali Tang, Yanli Xiong, 2017. Personal social relation extraction in Chinese based on feature selection of CHI, verb and noun. J. Application Research of Computers. 34, 6, 1631-1635.Google Scholar
- Brin S. 1998. Extracting patterns and relations from the world wide web. In International workshop on the world wide web and databases. Springer, Berlin, Heidelberg, 172-183. https://10.1007/10704656_11Google Scholar
- Yan Y, Okazaki N, Matsuo Y, 2009. Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. ACL, Suntec, 1021-1029.Google ScholarCross Ref
- Xianming Yao, Jianhou Gan, Jian Xu. 2019. Chinese open domain oriented n-ary entity relation extraction. J. CAAI Transactions on Intelligent Systems. 14, 3, 597-604.Google Scholar
- Zheng S, Wang F, Bao H, 2017. Joint extraction of entities and relations based on a novel tagging scheme.In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. ACL, Vancouver, 1227-1236. http://10.18653/v1/P17-1Google Scholar
Index Terms
- Research on Entity Relation Extraction Based on BiLSTM-CRF Classical Probability Word Problems
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