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Informal Mathematical Discourse Parsing with Conditional Random Fields

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Statistical Language and Speech Processing (SLSP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8791))

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Abstract

Discourse parsing for the Informal Mathematical Discourse (IMD) has been a difficult task because of the lack of data sets, partly because the Natural Language Processing (NLP) techniques must be adapted to informality of IMD. In this paper, we present an end-to-end discourse parser which is a sequential classifier of informal deductive argumentations (IDA) for Spanish. We design a discourse parser using sequence labeling based on CRFs (Conditional Random Fields). We use the CRFs on lexical, syntactic and semantic features extracted from a discursive corpus (MD-TreeBank: Mathematical Discourse TreeBank). In this article, we describe a Penn Discourse TreeBank (PDTB) styled End-to-End discourse parser into the Control Natural Languages (CNLs) context. Discourse parsing is focused from a discourse low level perspective in which we identify the IDA connectives avoiding complex linguistic phenomena. Our discourse parser performs parsing as a connective-level sequence labeling task and classifies several types of informal deductive argumentations into the mathematical proof.

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Notes

  1. 1.

    http://nlp.lsi.upc.edu/freeling/

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Authors and Affiliations

  1. EISC, Universidad del Valle, Cali, Colombia

    Raúl Ernesto Gutierrez de Piñerez Reyes & Juan Francisco Díaz-Frías

Authors
  1. Raúl Ernesto Gutierrez de Piñerez Reyes
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  2. Juan Francisco Díaz-Frías
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Corresponding author

Correspondence to Raúl Ernesto Gutierrez de Piñerez Reyes .

Editor information

Editors and Affiliations

  1. University Joseph Fourier, Grenoble, France

    Laurent Besacier

  2. Rovira i Virgili University, Tarragona, Spain

    Adrian-Horia Dediu

  3. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

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Gutierrez de Piñerez Reyes, R.E., Díaz-Frías, J.F. (2014). Informal Mathematical Discourse Parsing with Conditional Random Fields. In: Besacier, L., Dediu, AH., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2014. Lecture Notes in Computer Science(), vol 8791. Springer, Cham. https://doi.org/10.1007/978-3-319-11397-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-11397-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11396-8

  • Online ISBN: 978-3-319-11397-5

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