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Narrative structure of mathematical texts
 In preparation, available at http://www.macs.hw.ac.uk/~mm20
, 2007
"... Abstract. There are many styles for the narrative structure of a mathematical document. Each mathematician has its own conventions and traditions about labeling portions of texts (e.g., chapter, section, theorem or proof) and identifying statements according to their logical importance (e.g., theore ..."
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Abstract. There are many styles for the narrative structure of a mathematical document. Each mathematician has its own conventions and traditions about labeling portions of texts (e.g., chapter, section, theorem or proof) and identifying statements according to their logical importance (e.g., theorem is more important than lemma). Such narrative/structuring labels guide the reader’s navigation of the text and form the key components in the reasoning structure of the theory reflected in the text. We present in this paper a method to computerise the narrative structure of a text which includes the relationships between labeled text entities. These labels and relations are input by the user on top of their natural language text. This narrative structure is then automatically analysed to check its consistency. This automatic analysis consists of two phases: (1) checking the correct usage of labels and relations (i.e., that a “proof” justifies a “theorem ” but cannot justify an “axiom”) and (2) checking that the logical precedences in the document are selfconsistent. The development of this method was driven by the experience of computerising a number of mathematical documents (covering different authoring styles). We illustrate how such computerised narrative structure could be used for further manipulations, i.e. to build a skeleton of a formal document in a formal system like Mizar, Coq or Isabelle. 1
Abstract Computerizing Mathematical Text with
"... Mathematical texts can be computerized in many ways that capture differing amounts of the mathematical meaning. At one end, there is document imaging, which captures the arrangement of black marks on paper, while at the other end there are proof assistants (e.g., Mizar, Isabelle, Coq, etc.), which c ..."
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Cited by 1 (0 self)
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Mathematical texts can be computerized in many ways that capture differing amounts of the mathematical meaning. At one end, there is document imaging, which captures the arrangement of black marks on paper, while at the other end there are proof assistants (e.g., Mizar, Isabelle, Coq, etc.), which capture the full mathematical meaning and have proofs expressed in a formal foundation of mathematics. In between, there are computer typesetting systems (e.g., LATEX and Presentation MathML) and semantically oriented systems (e.g., Content MathML, OpenMath, OMDoc, etc.). The MathLang project was initiated in 2000 by Fairouz Kamareddine and Joe Wells with the aim of developing an approach for computerizing mathematical texts and knowledge which is flexible enough to connect the different approaches to computerization, which allows various degrees of formalization, and which is compatible with different logical frameworks (e.g., set theory, category theory, type theory, etc.) and proof systems. The approach is embodied in a computer representation, which we call MathLang, and associated software tools, which are being developed by ongoing work. Three Ph.D. students (Manuel Maarek (2002/2007), Krzysztof Retel (since 2004), and Robert Lamar (since 2006)) and over a dozen master’s degree and undergraduate students have worked on MathLang. The project’s progress and design choices are driven by the needs for computerizing real representative mathematical texts chosen from various
Example supplement for “Restoring natural language as a computerised mathematics input method”. Available at http://www.macs.hw.ac.uk/ ~rob
, 2007
"... Abstract. Due to a strict page limit for MKM 2007, it was necessary to remove the appendix from the paper [3] and instead provide its contents through an alternative medium. Distributed on the respective web sites of the paper’s authors, the contents are reproduced in this document. The reader will ..."
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Cited by 1 (1 self)
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Abstract. Due to a strict page limit for MKM 2007, it was necessary to remove the appendix from the paper [3] and instead provide its contents through an alternative medium. Distributed on the respective web sites of the paper’s authors, the contents are reproduced in this document. The reader will find herein an example in which the method of the above mentioned paper is applied to a typical text. The example is given first in a plain text, then as a version annotated according to the method proposed in this paper. A Ring theory example In the following three sections, there are to be found three different views of the same document. This example is a brief selection from [1], and provides a definition of an algebraic ring with some brief corollaries. It was chosen because it concisely exhibits most of our developments in a very accessible text. The observant reader will note that, although the examples throughout the main
Computerising Mathematical Text with MathLang
"... Mathematical texts can be computerised in many ways that capture differing amounts of the mathematical meaning. At one end, there is document imaging, which captures the arrangement of black marks on paper, while at the other end there are proof assistants (e.g., Mizar, Isabelle, Coq, etc.), which c ..."
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Mathematical texts can be computerised in many ways that capture differing amounts of the mathematical meaning. At one end, there is document imaging, which captures the arrangement of black marks on paper, while at the other end there are proof assistants (e.g., Mizar, Isabelle, Coq, etc.), which capture the full mathematical meaning and have proofs expressed in a formal foundation of mathematics. In between, there are computer typesetting systems (e.g., LATEX and Presentation MathML) and semantically oriented systems (e.g., Content MathML, OpenMath, OMDoc, etc.). The MathLang project was initiated in 2000 by Fairouz Kamareddine and Joe Wells with the aim of developing an approach for computerising mathematical texts which is flexible enough to connect the different approaches to computerisation, which allows various degrees of formalisation, and which is compatible with different logical frameworks (e.g., set theory, category theory, type theory, etc.) and proof systems. The approach is embodied in a computer representation, which we call MathLang, and associated software tools, which are being developed by ongoing work. Four Ph.D. students (Manuel Maarek (2002/2007), Krzysztof Retel (since 2004), Robert Lamar (since 2006)), and Christoph Zengler (since 2008) and over a dozen master’s degree and undergraduate
Algorithm, Performance
"... We report on the user requirements study and preliminary implementation phases in creating a digital library that indexes and retrieves educational materials on math. We first review the current approaches and resources for math retrieval, then report on the interviews of a small group of potential ..."
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We report on the user requirements study and preliminary implementation phases in creating a digital library that indexes and retrieves educational materials on math. We first review the current approaches and resources for math retrieval, then report on the interviews of a small group of potential users to properly ascertain their needs. While preliminary, the results suggest that metasearch and resource categorization are two basic requirements for a math search engine. In addition, we implement a prototype categorization system and show that the generic features work well in identifying the math contents from the webpage but perform less well at categorizing them. We discuss our long term goals, where we plan to investigate how math expressions and text search may be best integrated.
A Language Engineering Architecture for Processing Informal Mathematical Discourse
"... Abstract. We present a modular architecture for processing informal mathematical language as found in textbooks and mathematical publications. We point at its properties relevant in addressing three aspects of informal mathematical discourse: (i) the interleaved symbolic and natural language, (ii) ..."
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Abstract. We present a modular architecture for processing informal mathematical language as found in textbooks and mathematical publications. We point at its properties relevant in addressing three aspects of informal mathematical discourse: (i) the interleaved symbolic and natural language, (ii) the linguistic, domain, and notational context, and (iii) the imprecision of the informal language. The objective in the modular approach is to enable parameterisation of the system with respect to the natural language of the text and the mathematical domain of discourse. Key words: mathematical discourse, language processing 1