Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Androutsopoulos I and Malakasiotis P (2010) A Survey of Paraphrasing and Textual Entailment Methods. Journal of Artificial Intelligence Research, vol. 38, pp 135– 187
Berkow R (ed) (2000) Merck Manual Medisch handboek. Bohn Stafleu Van Loghum
Buitelaar P, Cimiano P, Magnini B (eds) (2005) Ontology learning from text: Methods, evaluation and applications. IOS Press
Cho P, Taira R, Kangarloo H (2003) Automatic segmentation of medical reports. In: Proc. of AMIA Symposium, pp 155–159
Cimiano P, Pivk A, Schmidt-Thieme L, Staab S (2005) Learning taxonomic relations from heterogeneous sources of evidence. In: Buitelaar P, Magnini B, Cimiano P (eds) Ontology Learning from Text: Methods, Applications, Evaluation, IOS Verlag
Dagan I, Glickman O (2004) Probabilistic textual entailment: Generic applied modeling of language variability. In: PASCAL Workshop on Learning Methods for Text Understanding and Mining, Grenoble
Declerck T, Lendvai P (2010) Towards a standardized linguistic annotation of the textual content of labels in knowledge representation systems. In: Calzolari N
Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D (eds) Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC’10), European Language Resources Association (ELRA), Valletta, Malta
Declerck T, Vela M (2005) Linguistic dependencies as a basis for the extraction of semantic relations. In: Wroe C, Gaizauskas R, Blaschke C (eds) ECCB’05 Workshop on Biomedical Ontologies and Text Processing
Guarino N, Welty C (2002) Evaluating ontological decisions with OntoClean. Commun ACM 45(2):61–65
Hearst M (1992) Automatic acquisition of hyponyms from large text corpora. In: Proc. of COLING
Hickl A, Williams J, Bensley J, Roberts K, Rink B, and Shi Y (2006) Recognizing Textual Entailment with LCC’s Groundhog System. In: Proc. of the Second PASCAL Recognizing Textual Entailment Challenge. Venice, Italy.
Katrenko S, Adriaans P (2006) Grammatical inference in practice: A case study in the biomedical domain. In: Grammatical Inference: Algorithms and Applications, vol 4201, Springer, pp 188–200
Kozareva Z, Hovy E, Riloff E (2009) Learning and evaluating the content and the structure of a term taxonomy. In: Proc. of AAAI 2009 spring symposium “Learning by Reading and Learning to Read”
Lendvai P (2008) Alignment-based expansion of textual database fields. In: Gelbukh A (ed) CICLing 2008. LNCS, vol. 4919, Springer Berlin / Heidelberg
Makagonov P, Figueroa A, Sboychakov K, Gelbukh A (2005) Learning a domain ontology from hierarchically structured texts. In: Proc. of ICML workshop on Learning and Extending Lexical Ontologies by using Machine Learning Methods, pp 50–57
McDowell L, Cafarella M (2008) Ontology-driven, unsupervised instance population. Journal of Web Semantics 6(3)
Mirkin S, Dagan I, Pad´o S (2010) Assessing the role of discourse references in entailment inference. In: Proc. of ACL
P´ery-Woodley MP, Scott D (2006) Introduction to the special issue on computational approaches to discourse and document structure. Traitment Automatique des Langues 47(2) Van der Plas L (2008) Automatic lexico-semantic acquisition for question answering. PhD thesis, Groningen
Spectrum (2003) Winkler Prins Medische Encyclopedie. Spectrum
Szpektor I, Shnarch R and Dagan I (2007) Instance-based Evaluation of Entailment Rule Acquisition. In: Proc. of ACL
Wang T, Li Y, Bontcheva K, Cunningham H,Wang J (2006) Automatic extraction of hierarchical relations from text. In: Lecture Notes in Computer Science, Springer Van Zaanen M (2001) Bootstrapping structure into language: Alignment-based learning. PhD thesis, School of Computing, University of Leeds, UK
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lendvai, P. (2011). Towards a Discourse-driven Taxonomic Inference Model. In: van den Bosch, A., Bouma, G. (eds) Interactive Multi-modal Question-Answering. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17525-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-17525-1_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17524-4
Online ISBN: 978-3-642-17525-1
eBook Packages: Computer ScienceComputer Science (R0)