Extraction of a group-pair relation: problem-solving relation from web-board documents

This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical representation benefits non-professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem-concept EDU boundary and a solving-concept EDU boundary as two event-explanation groups, and how to determine the Problem-Solving relation between these two event-explanation groups. Therefore, we apply word co-occurrence to identify a problem-concept EDU and a solving-concept EDU, and machine-learning techniques to solve a problem-concept EDU boundary and a solving-concept EDU boundary. We propose using k-mean and Naïve Bayes to determine the Problem-Solving relation between the two event-explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group-pair relation extraction with high accuracy.

[1]  L BergerAdam,et al.  A maximum entropy approach to natural language processing , 1996 .

[2]  Asanee Kawtrakul,et al.  Thai Named Entity Extraction by incorporating Maximum Entropy Model with Simple Heuristic Information , 2004 .

[3]  Roxana Gîrju,et al.  Automatic Detection of Causal Relations for Question Answering , 2003, ACL 2003.

[4]  Chaveevan Pechsiri,et al.  Explanation knowledge graph construction through causality extraction from texts , 2010 .

[5]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[6]  Sung-Hyon Myaeng,et al.  Procedural Knowledge Extraction on MEDLINE Abstracts , 2011, AMT.

[7]  Pierre Hansen,et al.  NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.

[8]  Barbara Rosario,et al.  Extraction of semantic relations from bioscience text , 2005 .

[9]  Om P. Damani,et al.  Lexical Co-occurrence, Statistical Significance, and Word Association , 2011, EMNLP.

[10]  Enrico Motta,et al.  SemSearch: A Search Engine for the Semantic Web , 2006, EKAW.

[11]  Namhee Kwon,et al.  Maximum Entropy Models for FrameNet Classification , 2003, EMNLP.

[12]  L. Hardin,et al.  Problem-solving concepts and theories. , 2003, Journal of veterinary medical education.

[13]  Se-Jong Kim,et al.  Method of Extracting Is-A and Part-Of Relations Using Pattern Pairs in Mass Corpus , 2009, PACLIC.

[14]  Oren Etzioni,et al.  Identifying Relations for Open Information Extraction , 2011, EMNLP.

[15]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[16]  Thomas Joseph,et al.  A pipeline to extract drug-adverse event pairs from multiple data sources , 2014, BMC Medical Informatics and Decision Making.

[17]  Christopher S. G. Khoo,et al.  Semantic relations in information science , 2006, Annu. Rev. Inf. Sci. Technol..

[18]  Natalia Konstantinova,et al.  Review of Relation Extraction Methods: What Is New Out There? , 2014, AIST.

[19]  James Pustejovsky,et al.  The syntax of event structure , 1991, Cognition.

[20]  Maarten van Someren,et al.  Using Local Alignments for Relation Recognition , 2010, J. Artif. Intell. Res..

[21]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[22]  Pierre Zweigenbaum,et al.  Automatic extraction of semantic relations between medical entities: a rule based approach , 2011, J. Biomed. Semant..

[23]  I. Csiszár.  Maxent, Mathematics, and Information Theory , 1996 .

[24]  Daniel Marcu,et al.  Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory , 2001, SIGDIAL Workshop.

[25]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[26]  Yorick Wilks,et al.  Subject-Dependent Co-Occurence and Word Sense Disambiguation , 1991, ACL.

[27]  Chaveevan Pechsiri,et al.  Explanation Knowledge Graph Construction Through Causality Extraction from Texts , 2010, Journal of Computer Science and Technology.

[28]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .