This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
July 2005 (vol. 27 no. 7)
pp. 1075-1086
ASCII Text
x 
 
Roberto Navigli, Paola Velardi, "Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 7, pp. 1075-1086, July, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.149,
author = {Roberto Navigli and Paola Velardi},
title = {Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {7},
issn = {0162-8828},
year = {2005},
pages = {1075-1086},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.149},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IS - 7
SN - 0162-8828
SP1075
EP1086
EPD - 1075-1086
A1 - Roberto Navigli,
A1 - Paola Velardi,
PY - 2005
KW - Index Terms- Natural language processing
KW - ontology learning
KW - structural pattern matching
KW - word sense disambiguation.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in this field would have a significant impact on many relevant Web-based applications, such as Web information retrieval, improved access to Web services, information extraction, etc. Early approaches to WSD, based on knowledge representation techniques, have been replaced in the past few years by more robust machine learning and statistical techniques. The results of recent comparative evaluations of WSD systems, however, show that these methods have inherent limitations. On the other hand, the increasing availability of large-scale, rich lexical knowledge resources seems to provide new challenges to knowledge-based approaches. In this paper, we present a method, called structural semantic interconnections (SSI), which creates structural specifications of the possible senses for each word in a context and selects the best hypothesis according to a grammar G, describing relations between sense specifications. Sense specifications are created from several available lexical resources that we integrated in part manually, in part with the help of automatic procedures. The SSI algorithm has been applied to different semantic disambiguation problems, like automatic ontology population, disambiguation of sentences in generic texts, disambiguation of words in glossary definitions. Evaluation experiments have been performed on specific knowledge domains (e.g., tourism, computer networks, enterprise interoperability), as well as on standard disambiguation test sets.

[1] N. Ide and J. Veronis, “Introduction to the Special Issue on Word Sense Disambiguation,” Computational Linguistics, vol. 24, no. 1, Mar. 1998.
[2] Y. Wilks, “A Preferential Pattern-Seeking Semantics for Natural Language Inference,” Artificial Intelligence, vol. 6, pp. 53-74, 1978.
[3] R. Schank and R. Abelson, Scripts, Plans, Goals, and Understanding. Hillsdale, N.J.: Lawrence Erlbaum, 1977.
[4] R. Mihalcea and D.I. Moldovan, “A Highly Accurate Bootstrapping Algorithm for Word Sense Disambiguation,” Int'l J. Artificial Intelligence Tools, vol. 10, nos. 1-2, pp. 5-21, 2001.
[5] P. Resnik, “Using Information Content to Evaluate Semantic Similarity In A Taxonomy,” Proc. Int'l Joint Confs. Artificial Intelligence (IJCAI), 1995.
[6] R. Krovetz and W.B. Croft, “Word Sense Disambiguation Using Machine Readable Dictionaries,” Proc. 12th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 127-136, 1989.
[7] D. Yarowski, “Word-Sense Disambiguation Using Statistical Models of the Roget's Categories Trained on Large Corpora,” Proc. 14th Int'l Conf. Computational Linguistics (COLING-92), pp. 454-460, 1992.
[8] W. Gale, K. Church, and D. Yarowsky, “One Sense per Discourse,” Proc. DARPA Speech and Natural Language Workshop, pp. 233-237, Feb. 1992.
[9] W. Gale, K. Church, and D. Yarowsky, “A Method for Disambiguating Word Senses in a Corpus,” Computer and the Humanities, vol. 26, pp. 415-439, 1992.
[10] J. Gonzalo, F. Verdejo, I. Chugur, and J. Cigarr'an, “Indexing with WordNet Synsets Can Improve Text Retrieval,” Proc. COLING/ACL '98 Workshop Usage of WordNet for NLP, 1998.
[11] T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web,” Scientific Am., May2001.
[12] OpenCyc home page, http:/www.opencyc.org/, 2005.
[13] WordNet: An Electronic Lexical Database, C. Fellbaum, ed. MIT Press, 1998.
[14] FrameNet home page, http://www.icsi.berkeley.eduframenet/, 2005.
[15] K.S. Fu, Syntactic Pattern Recognition and Applications. Englewood Cliffs, N.J.: Prentice Hall, 1982.
[16] Syntactic and Structural Pattern Recognition: Theory and Applications, H. Bunke and A. Sanfeliu, eds. World Scientific, 1990.
[17] N. Guarino and C. Welty, “Evaluating Ontological Decisions with OntoClean,” Comm. ACM, vol. 45, no. 2, pp. 61-65, 2002.
[18] Second Global WordNet Conf., Jan. 2004, http://www.fi.muni. czgwc2004/.
[19] B. Magnini and G. Cavaglià, “Integrating Subject Field Codes into WordNet,” Proc. Second Int'l Conf. Language Resources and Evaluation (LREC2000), 2000.
[20] G.A. Miller, M. Chodorow, S. Landes, C. Leacock, and R.G. Thomas, “Using a Semantic Concordance for Sense Identification,” Proc. ARPA Human Language Technology Workshop, pp. 240-243, 1994.
[21] H.T. Ng and H.B. Lee, “Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach,” Proc. 34th Ann. Meeting Assoc. for Computational Linguistics, 1996.
[22] Oxford Collocations, D. Lea, ed. Oxford Univ. Press, 2002.
[23] Longman Language Activator, K. Longman, ed. Pearson Education, 2003.
[24] R. Navigli, “Semi-Automatic Extension of Large-Scale Linguistic Knowledge Bases,” Proc. 18th FLAIRS Int'l Conf., May 2005.
[25] R. Mihalcea and D. Moldovan, “eXtended WordNet: Progress Report,” Proc. Workshop WordNet and Other Lexical Resources, 2001.
[26] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools with Java Implementations. San Francisco: Morgan Kaufmann, 2000.
[27] R. Navigli and P. Velardi, “Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites,” Computational Linguistics, MIT Press, June 2004.
[28] K. Litkowski, “Senseval-3 Task: Word Sense Disambiguation of WordNet Glosses,” Proc. Senseval-3 Third Int'l Workshop Evaluation of Systems for Semantic Analysis of Texts, pp. 13-16, July 2004.
[29] R. Navigli and P. Velardi, “An Analysis of Ontology-Based Query Expansion Strategies,” Proc. Workshop Adaptive Text Extraction and Mining, Sept. 2003.
[30] D. McCarthy, R. Koeling, J. Weeds, and J. Carroll, “Finding Predominant Word Senses in Untagged Text,” Proc. 42nd Ann. Meeting Assoc. for Computational Linguistics, July 2004.
[31] Proc. EKAW*04 Workshop Core Ontologies in Ontology Eng., http://sunsite.informatik.rwth-aachen.de/ Publications/CEUR-WSVol-118/, 2004.
[32] R. Navigli, “Supporting Large-Scale Knowledge Acquisition with Structural Semantic Interconnections,” Proc. AAAI Spring Symp., 2005.

Index Terms:
Index Terms- Natural language processing, ontology learning, structural pattern matching, word sense disambiguation.
Citation:
Roberto Navigli, Paola Velardi, "Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 7, pp. 1075-1086, July 2005, doi:10.1109/TPAMI.2005.149
Usage of this product signifies your acceptance of the Terms of Use.