Towards a Discourse-driven Taxonomic Inference Model

  • Piroska LendvaiEmail author
Part of the Theory and Applications of Natural Language Processing book series (NLP)


This chapter describes ongoing work, the goal of which is to create a discourse-driven inference model, as well as to construct resources using such a model. The data process consists of texts from two encyclopedias of the medical domain–stylistic properties characteristic of encyclopedia entries constitute the mechanisms underlying the inference model, such as layout-based features alongside with semantic (conceptual) document structuring. Three parts of the model are explained in detail, providing experimental results that are based on language processing techniques: (i) identifying taxonomic document structure by machine learning; (ii) discourse-driven construction of text–hypothesis pairs for examining types of textual entailment; (iii) semi-supervised harvesting of lexico-semantic patterns that connect medical concept types.


Bacterial Meningitis Viral Meningitis Sentence Topic Conceptual Taxonomy Question Answering System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Research Institute for LinguisticsHungarian Academy of SciencesBudapestHungary

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