The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Design of a multi-lingual, parallel-processing statistical parsing engine
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Introduction to Information Retrieval
Introduction to Information Retrieval
Narrative Structure of Mathematical Texts
Calculemus '07 / MKM '07 Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference
Discourse Connective Argument Identification with Connective Specific Rankers
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Attribution and the (non-)alignment of syntactic and discourse arguments of connectives
CorpusAnno '05 Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky
The naproche project controlled natural language proof checking of mathematical texts
CNL'09 Proceedings of the 2009 conference on Controlled natural language
MathAbs: a representational language for mathematics
Proceedings of the 8th International Conference on Frontiers of Information Technology
Preprocessing of informal mathematical discourse in context ofcontrolled natural language
Proceedings of the 21st ACM international conference on Information and knowledge management
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The lack of specific data sets makes difficult the discourse parsing for Informal Mathematical Discourse (IMD). In this paper, we propose a data driven approach to identify arguments and connectives in an IMD structure within the context of Controlled Natural Language (CNL). Our approach follows a low-level discourse parsing under Peen Discourse TreeBank (PDTB) guidelines. Three classifiers have been trained: one that identifies the Arg2, other that locates the relative position of Arg1 and a third that identifies the (Arg1 and Arg2) arguments of each connective. These classifiers are instances of Support Vector Machines (SVMs), fed from an own Mathematical TreeBank. Finally, our approach defines an End-to-End discourse parser into IMD, whose results will be used to classify of informal deductive proofs via the low level discourse in IMD processing.