FP-TREE MOTIVATED SYSTEM FOR INFORMATION RETRIEVAL USING AN ABSTRACTION PATH-BASED INVERTED INDEX

ion Paths To illustrate the idea of an abstraction path, the WordNet-derived abstraction path corresponding to a sense of the word ‘basketball’ is:

[1]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[2]  Dominic Widdows,et al.  A Graph Model for Unsupervised Lexical Acquisition , 2002, COLING.

[3]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[4]  Benjamin C. M. Fung,et al.  Hierarchical Document Clustering using Frequent Itemsets , 2003, SDM.

[5]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[6]  Michael Sussna,et al.  Word sense disambiguation for free-text indexing using a massive semantic network , 1993, CIKM '93.

[7]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[8]  George A. Miller,et al.  WordNet: A Lexical Database for the English Language , 2002 .

[9]  Philip Calvert,et al.  Encyclopedia of Data Warehousing and Mining , 2006 .

[10]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[11]  Ellen M. Voorhees,et al.  TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing) , 2005 .

[12]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[13]  Ted Pedersen,et al.  Maximizing Semantic Relatedness to Perform Word Sense Disambiguation , 2005 .

[14]  Rafal A. Angryk,et al.  Measuring semantic similarity using wordnet-based context vectors , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[16]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[17]  M. Shahriar Hossain,et al.  GDClust: A Graph-Based Document Clustering Technique , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[18]  Manu Konchady.  Text Mining Application Programming , 2006 .

[19]  Ronen Feldman,et al.  Book Reviews: The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data by Ronen Feldman and James Sanger , 2008, CL.

[20]  Monika Akbar,et al.  Frequent pattern-growth approach for document organization , 2008, ONISW '08.

[21]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[22]  Ellen M Voorhees.  Question answering in TREC , 2001, CIKM '01.

[23]  Hui Xiong,et al.  On the strength of hyperclique patterns for text categorization , 2007, Inf. Sci..

[24]  Naftali Tishby,et al.  Unsupervised document classification using sequential information maximization , 2002, SIGIR '02.