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Mathematical Language Processing : = Deep Learning Representations and Inference Over Mathematical Text.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical Language Processing :/
其他題名:	Deep Learning Representations and Inference Over Mathematical Text.
作者:	Ferreira, Deborah Mendes.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
Contained By:	Dissertations Abstracts International84-06B.
標題:	Language. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30193936click for full text (PQDT)
ISBN:	9798358407329

Mathematical Language Processing : = Deep Learning Representations and Inference Over Mathematical Text.
Ferreira, Deborah Mendes.

Mathematical Language Processing :Deep Learning Representations and Inference Over Mathematical Text. - 1 online resource (154 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: B.

Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2022.

Includes bibliographical references

The articulation of mathematical arguments is a fundamental part of scientific reasoning and communication. Across many disciplines, expressing relations and interdependencies between quantities (usually in an equational form) is at the centre of scientific argumentation. One can easily find examples of mathematical discourse across different scientific contributions and textbooks. Nevertheless, despite its importance, the application of contemporary NLP models for performing inference over mathematical text remains under-explored, especially when compared with other advances in natural language processing and domain-specific text mining (e.g. biomedical text). In this work, we contribute to the area of Mathematical Language Processing, which addresses problems in the intersection of Natural Language and Mathematics. While several aspects of the mathematical discourse are still unexplored in this field, we have opted to focus on three main dimensions: (i) defining an evaluation framework for mathematical natural language inference; (ii) learning sentence-level representations of mathematical statements; (iii) leveraging argumentation-level premise-claim discourse relations between mathematical statements.The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. In this work, we propose the Natural Language Premise Selection task, together with a new dataset, which consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be helpful to prove a particular statement. Another fundamental requirement towards mathematical language understanding is the creation of models able to represent variables meaningfully. In this work, we propose different deep learning based techniques to address such issues, identifying the challenges associated with such tasks and pave the way for future work in this field.

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

Mode of access: World Wide Web

ISBN: 9798358407329Subjects--Topical Terms:

643551
Language.
Index Terms--Genre/Form:

542853
Electronic books.

Mathematical Language Processing : = Deep Learning Representations and Inference Over Mathematical Text.
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504 $a Includes bibliographical references
520 $a The articulation of mathematical arguments is a fundamental part of scientific reasoning and communication. Across many disciplines, expressing relations and interdependencies between quantities (usually in an equational form) is at the centre of scientific argumentation. One can easily find examples of mathematical discourse across different scientific contributions and textbooks. Nevertheless, despite its importance, the application of contemporary NLP models for performing inference over mathematical text remains under-explored, especially when compared with other advances in natural language processing and domain-specific text mining (e.g. biomedical text). In this work, we contribute to the area of Mathematical Language Processing, which addresses problems in the intersection of Natural Language and Mathematics. While several aspects of the mathematical discourse are still unexplored in this field, we have opted to focus on three main dimensions: (i) defining an evaluation framework for mathematical natural language inference; (ii) learning sentence-level representations of mathematical statements; (iii) leveraging argumentation-level premise-claim discourse relations between mathematical statements.The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. In this work, we propose the Natural Language Premise Selection task, together with a new dataset, which consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be helpful to prove a particular statement. Another fundamental requirement towards mathematical language understanding is the creation of models able to represent variables meaningfully. In this work, we propose different deep learning based techniques to address such issues, identifying the challenges associated with such tasks and pave the way for future work in this field.
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