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Analysis of Mixed Natural and Symbolic Language Input in Mathematical Dialogs
, 2004
"... Discourse in formal domains, such as mathematics, is characterized by a mixture of telegraphic natural language and embedded (semi)formal symbolic mathematical expressions. We present language phenomena observed in a corpus of dialogs with a simulated tutorial system for proving theorems as evidenc ..."
Abstract

Cited by 9 (4 self)
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Discourse in formal domains, such as mathematics, is characterized by a mixture of telegraphic natural language and embedded (semi)formal symbolic mathematical expressions. We present language phenomena observed in a corpus of dialogs with a simulated tutorial system for proving theorems as evidence for the need for deep syntactic and semantic analysis. We propose an approach to input understanding in this setting. Our goal is a uniform analysis of inputs of different degree of verbalization: ranging from symbolic alone to fully worded mathematical expressions.
Lexicalsemantic interpretation of language input in mathematical dialogs
 In Proceedings of the ACL 2nd Workshop on Text Meaning and Interpretation
, 2004
"... Discourse in formal domains, such as mathematics, is characterized by a mixture of telegraphic natural language and embedded (semi)formal symbolic mathematical expressions. Due to the lack of empirical data, little is known about the suitability of input analysis methods for mathematical discourse ..."
Abstract

Cited by 2 (2 self)
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Discourse in formal domains, such as mathematics, is characterized by a mixture of telegraphic natural language and embedded (semi)formal symbolic mathematical expressions. Due to the lack of empirical data, little is known about the suitability of input analysis methods for mathematical discourse in a dialog setting. We present an input understanding method for a tutoring system teaching mathematical theorem proving. The adopted deep analysis strategy is motivated by the complexity of the language phenomena observed in a corpus collected in a WizardofOz experiment. Our goal is a uniform input interpretation, in particular, considering different degrees of formality of natural language verbalizations. 1
Managing mathematical texts with OWL and their graphical representation
"... Mathematical knowledge contained in scientific digital publications poses a challenge for intelligent retrieval mechanisms. Many current approaches use statistical (e.g. Google) or natural language processing methods to find correlations in texts and annotate texts semantically. However both kinds o ..."
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Mathematical knowledge contained in scientific digital publications poses a challenge for intelligent retrieval mechanisms. Many current approaches use statistical (e.g. Google) or natural language processing methods to find correlations in texts and annotate texts semantically. However both kinds of approaches face the problem of extracting and processing knowledge from mathematical equations. The presented system is based on natural language processing techniques, and benefits from characteristic linguistic structures defined by the language used in mathematical texts. It accumulates extracted information snippets from texts, symbols, and equations in knowledge bases. These knowledge bases provide the foundation for the information retrieval. This article describes the concepts and the prototypical technical implementation.
mArachna – Ontology Engineering for Mathematical Natural Language Texts
"... The knowledge contained in the growing number of scientific digital publications, particularly over the internet creates new demands for intelligent retrieval mechanisms. One basic approach in support of such retrieval mechanisms is the generation of semantic annotation, based on ontologies describi ..."
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The knowledge contained in the growing number of scientific digital publications, particularly over the internet creates new demands for intelligent retrieval mechanisms. One basic approach in support of such retrieval mechanisms is the generation of semantic annotation, based on ontologies describing both the field and the structure of the texts themselves. Many current approaches use statistical methods similar to the ones employed by Google to find correlations within the texts. This approach neglects the additional information provided in the upper ontology used by the author. mArachna, however, is based on natural language processing techniques, taking advantage of characteristic linguistic structures defined by the language used in mathematical texts. It stores the extracted knowledge in a knowledge base, creating a lowlevel ontology of mathematics and mapping this ontology onto the structure of the knowledge base. The following article gives an overview over the concepts and technical implementation of the mArachna prototype. 1
Information Society Technologies
"... This report comes out of the attempt to translate the HELM CIC format into OMDoc by XslT style sheets. The experiment and the resulting style sheets are covered in the companion document D2c of this report. These style sheets transform the the lowlevel XML description of the library of the Coq Proo ..."
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This report comes out of the attempt to translate the HELM CIC format into OMDoc by XslT style sheets. The experiment and the resulting style sheets are covered in the companion document D2c of this report. These style sheets transform the the lowlevel XML description of the library of the Coq Proof Assistant to the version of OMDoc described in this report. Currently, the style sheets only cover a part of the ultimate transformation, covered by the original HELM format. This part consists in adding inner types (as contentMathML expressions) to the terms exported from Coq and transforming the proof structure. The generation of natural language, linebreaking considerations, etc. will be implemented later in the task T2.5
Natural Language Processing of Mathematical Texts in mArachna
"... AbstractmArachna is a technical framework designed for the extraction of mathematical knowledge from natural language texts. mArachna avoids the problems typically encountered in automatedreasoning based approaches through the use of natural language processing techniques taking advantage of the s ..."
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AbstractmArachna is a technical framework designed for the extraction of mathematical knowledge from natural language texts. mArachna avoids the problems typically encountered in automatedreasoning based approaches through the use of natural language processing techniques taking advantage of the strict formalized language characterizing mathematical texts. Mathematical texts possess a strict internal structuring and can be separated into text elements (entities) such as definitions, theorems etc. These entities are the principal carriers of mathematical information. In addition, Entities show a characteristic coupling between the presented information and their internal linguistic structure, well suited for natural language processing techniques. Taking advantage of this structure, mArachna extracts mathematical relations from texts and integrates them into a knowledge base. Identifying sub