En este subepígrafe presentaremos las principales medidas para evaluar la calidad de la clasificación de la desambiguación y mostraremos cómo se comportan estas medidas al clasificar 405 oraciones de Semcor con un total de 3041 términos desambiguados.
Las medidas utilizadas para la evaluación del algoritmo de WSD se muestran en las expresiones (3.1) y (3.2) (Vázquez, 2009): (3.1) (3.2)
En la Tabla 3.13 se presentan los resultados de las medidas de evaluación de la desambiguación a 405 oraciones de estudio provenientes del corpus Semcor al aplicarles el algoritmo RST- Disambiguation.
Tabla 3.13 Resultados de la validación de RST-Disambiguation al desambiguar 405 oraciones de Semcor. Medida Resultado
Precisión 0.503 Exactitud 0.503
Finalmente, se comparó el algoritmo RST-Disambiguation con el método propuesto por (Anaya- Sánchez et al., 2007) respecto a la medida Exactitud, ya que este es el resultado que se encuentra disponible para este algoritmo de referencia. En la Tabla 3.14 es posible observar que el algoritmo RST-Disambiguation supera al algoritmo propuesto por (Anaya-Sánchez et al., 2007) en cuanto a la exactitud de los resultados de la desambiguación.
77 Tabla 3.14 Valores de Exactitud de RST-Disambiguation y el algoritmo propuesto (Anaya-Sánchez et al., 2007).
Método de WSD Exactitud Método propuesto en (Anaya-Sánchez et al., 2007) 0.472
RST-Disambiguation 0.503
3.5 Conclusiones parciales
La biblioteca UnsupervisedWSD integra varios métodos que permiten la desambiguación de términos. Está escrita en Java y compuesta por los paquetes: disambiguation, clustering, RST y
util. Permite realizar estudios comparativos y estudiar los métodos para desambiguar y así tomar decisiones de cuál incluir en aplicaciones de minería de opinión.
Los ejemplos desarrollados permitieron ilustrar que el algoritmo RST-Disambiguation en algunos casos obtiene resultados similares a los métodos modificados a partir de la propuesta de (Anaya- Sánchez et al., 2007) con una menor cantidad de iteraciones. En otros casos, el algoritmo RST- Disambiguation logra identificar los sentidos asociados a cada término que mejor se ajustan al contexto. Por tanto, el algoritmo RST-Disambiguation en los casos estudiados supera los algoritmos de referencia o iguala sus resultados con un menor número de iteraciones.
Finalmente, la validación realizada a partir de 405 oraciones del corpus Semcor evidenció que RST-Disambiguation tiene un buen comportamiento al obtener 0.503 de exactitud y precisión, superando el valor 0.472 de la propuesta de (Anaya-Sánchez et al., 2007).
78
Conclusiones
Como resultado de esta investigación se diseñaron métodos que modifican la propuesta de (Anaya-Sánchez et al., 2007) para la desambiguación y se creó el método RST-Disambiguation que permite desambiguar de manera no supervisada el sentido de las palabras, basado en agrupamientos y en la teoría de los conjuntos aproximados, logrando desambiguar de manera efectiva y eficiente; cumpliéndose de esta forma el objetivo general planteado, ya que:
1. Los métodos supervisados reportan mejores resultados al desambiguar; sin embargo, requieren el aprendizaje de corpora previamente clasificados, lo cual es difícil de obtener, además se entrenan para un tema específico y por tanto son menos generales. Los métodos no supervisados no requieren de corpora previamente clasificados pero reportan valores más bajos de calidad de la desambiguación. Dentro de las aproximaciones no supervisadas se desatacan aquellas basadas en grafos y que consultan recursos léxicos para enriquecer el análisis.
2. Las modificaciones realizadas al método de desambiguación propuesto en (Anaya- Sánchez et al., 2007) permitieron aplicar los algoritmos de agrupamiento Estrella Generalizado y Estrella Condensado para el agrupamiento del sentido de los términos y por tanto desambiguar de manera más rápida, e incluso en algunos casos superando la precisión.
3. El método RST-Disambiguation explota las ventajas de la Teoría de los Conjuntos Aproximados para identificar los mejores grupos de sentidos de las palabras e incorporar aquellos sentidos correspondientes a términos no cubiertos. De esta forma se obtienen mejores resultados en la desambiguación y en una menor cantidad de iteraciones.
4. La biblioteca UnsupervisedWSD integra varios métodos que permiten la desambiguación de términos. Está escrita en Java y compuesta por los paquetes: disambiguation,
79
clustering, RST y util. Permite realizar estudios comparativos y estudiar los métodos para desambiguar y así tomar decisiones de cuál incluir en aplicaciones de minería de opinión. 5. RST-Disambiguation en algunos casos obtiene resultados similares a los métodos
modificados a partir de la propuesta de (Anaya-Sánchez et al., 2007) con una menor cantidad de iteraciones. En otros casos, logra identificar los sentidos asociados a cada término que mejor se ajustan al contexto. La validación realizada a partir de 405 oraciones del corpus Semcor evidenció que RST-Disambiguation tiene un buen comportamiento al obtener 0.503 de exactitud y precisión, superando el valor 0.472 de la propuesta de (Anaya-Sánchez et al., 2007).
80
Recomendaciones
Derivadas del estudio realizado, así como de las conclusiones generales emanadas del mismo, se recomienda:
Utilizar las ventajas de la biblioteca UnsupervisedWSD en un sistema de minería de opinión, garantizando de esta forma una correcta desambiguación de los términos a minar. Agregar a la biblioteca UnsupervisedWSD nuevos algoritmos de agrupamiento con el
objetivo de obtener grupos de sentidos que mejor caractericen a las oraciones y que sean obtenidos en un menor tiempo.
81
Referencias bibliográficas
AGIRRE, E. & EDMONDS, P. G. 2006.
Word sense disambiguation:
Algorithms and applications, Springer Science+ Business Media.
AGIRRE, E. & MARTINEZ, D. Exploring automatic word sense
disambiguation with decision lists and the Web. Proceedings of the
COLING-2000 Workshop on Semantic Annotation and Intelligent
Content, 2000. Association for Computational Linguistics, 11-19.
AGIRRE, E. & MARTINEZ, D. Learning class-to-class selectional
preferences. Proceedings of the 2001 workshop on Computational
Natural Language Learning-Volume 7, 2001. Association for
Computational Linguistics, 3.
AGIRRE, E. & RIGAU, G. Word sense disambiguation using conceptual
density. Proceedings of the 16th conference on Computational
linguistics-Volume 1, 1996. Association for Computational Linguistics,
16-22.
AGIRRE, E. & STEVENSON, M. 2006. Knowledge sources for WSD.In
Word Sense Disambiguation: Algorithms and Applications.
Springer,
,
217-251.
ALONSO, A. G., SUÁREZ, A. P. & PAGOLA, J. E. M. 2007. ACONS: a new
algorithm for clustering documents. Progress in Pattern Recognition,
Image Analysis and Applications. Springer.
ANAYA-SÁNCHEZ, H., PONS-PORRATA, A. & BERLANGA-LLAVORI, R.
TKB-UO: using sense clustering for WSD. Proceedings of the 4th
International Workshop on Semantic Evaluations, 2007. Association
for Computational Linguistics, 322-325.
ARCO, L. 2008. Agrupamiento basado en la intermediación diferencial y su
valoración utilizando la teoría de los conjuntos aproximados.,
82
ASLAM, J. A., PELEKHOV, E. & RUS, D. 2004. The Star Clustering
Algorithm for Static and Dynamic Information Organization. J. Graph
Algorithms Appl., 8, 95-129.
ATKINS, S. Tools for computer-aided corpus lexicography: The Hector
project. Acta Linguistica Hungarica, 1993.
BANERJEE, S. & PEDERSEN, T. 2002. An adapted Lesk algorithm for
word sense disambiguation using WordNet. Computational linguistics
and intelligent text processing. Springer.
BANERJEE, S. & PEDERSEN, T. Extended gloss overlaps as a measure of
semantic relatedness. IJCAI, 2003. 805-810.
BARZILAY, R. & ELHADAD, M. Using lexical chains for text summarization.
Proceedings of the ACL workshop on intelligent scalable text
summarization, 1997. Madrid, Spain;, 10-17.
BAZAN, J., NGUYEN, H. S. & SZCZUKA, M. 2004. A view on rough set
concept approximations. Fundamenta Informatica, 59, 107-118.
BERNARD, J. R. L., ED. 1986. Macquarie Thesaurus. Macquarie.
BERNERS-LEE, T., HENDLER, J. & LASSILA, O. 2001. The semantic web.
Scientific american, 284, 28-37.
BLACK, E. 1988. An experiment in computational discrimination of English
word senses. IBM Journal of research and development, 32, 185-194.
BOBILLO,
F.,
DELGADO,
M.
&
GÓMEZ-ROMERO,
J.
2008.
Representation of context-dependant knowledge in ontologies: A
model and an application.
Expert Systems with Applications, 35,
1899-1908.
BORDAG, S. Word Sense Induction: Triplet-Based Clustering and
Automatic Evaluation. EACL, 2006.
BRANTS, T. & FRANZ, A. 2006. {Web 1T 5-gram Version 1}.
BRODY, S., NAVIGLI, R. & LAPATA, M. Ensemble methods for
unsupervised WSD. Proceedings of the 21st International
Conference on Computational Linguistics and the 44th annual
meeting of the Association for Computational Linguistics, 2006.
Association for Computational Linguistics, 97-104.
83
BRUCE, R. F. & WIEBE, J. M. 1999. Decomposable modeling in natural
language processing. Computational linguistics, 25, 195-207.
BUDANITSKY, A. & HIRST, G. 2006. Evaluating wordnet-based measures
of lexical semantic relatedness. Computational linguistics, 32, 13-47.
BUNKE, H. & SANFELIU, A. 1990.
Syntactic and structural pattern
recognition: theory and applications, World Scientific.
CLEAR, J. H. The British national corpus. The digital word, 1993. MIT
Press, 163-187.
COTTRELL, G. W. 1989. A Connectionist Approach to Word Sense
Disambiguation.
CRUSE, D. A. 1986. Lexical semantics, Cambridge University Press.
CUADROS, M. & RIGAU, G. Quality assessment of large scale knowledge
resources. Proceedings of the 2006 Conference on Empirical
Methods in Natural Language Processing, 2006. Association for
Computational Linguistics, 534-541.
CHARNIAK, E., BLAHETA, D., GE, N., HALL, K., HALE, J. & JOHNSON,
M. 2000. Bllip 1987-89 WSJ corpus release 1. Linguistic Data
Consortium LDC2000T43. ISBN 1-58563-165-5.
CHKLOVSKI, T. & MIHALCEA, R. Building a sense tagged corpus with
open mind word expert. Proceedings of the ACL-02 workshop on
Word sense disambiguation: recent successes and future directions-
Volume 8, 2002. Association for Computational Linguistics, 116-122.
DAELEMANS, W., VAN DEN BOSCH, A. & ZAVREL, J. 1999. Forgetting
exceptions is harmful in language learning. Machine Learning, 34, 11-
41.
DECADT, B., HOSTE, V., DAELEMANS, W. & VAN DEN BOSCH, A.
GAMBL, genetic algorithm optimization of memory-based WSD.
Senseval-3: Third International Workshop on the Evaluation of
Systems for the Semantic Analysis of Text, 2004. 108-112.
DEMPSTER, A. P., LAIRD, N. M. & RUBIN, D. B. 1977. Maximum
likelihood from incomplete data via the EM algorithm. Journal of the
84
ESCUDERO, G., MÀRQUEZ, L. & RIGAU, G. 2000b. Naive Bayes and
exemplar-based approaches to word sense disambiguation revisited.
arXiv preprint cs/0007011.
ESCUDERO, G., MÀRQUEZ, L., RIGAU, G. & SALGADO, J. G. 2000c. On
the portability and tuning of supervised word sense disambiguation
systems.
FELLBAUM, C. 2010. WordNet: An electronic lexical database. 1998.
WordNet is available from http://www. cogsci. princeton. edu/wn.
FELLBAUM, C., ED. 1998. WordNet: An Electronic Database.
FLORIAN, R., CUCERZAN, S., SCHAFER, C. & YAROWSKY, D. 2002.
Combining classifiers for word sense disambiguation.
Natural
Language Engineering, 8, 327-341.
FOWLER, M. & SCOTT, K. 1997. UML distilled: applying the standard
object modeling language, 1997. Addison, Wesley, Longman.
FRAKES, W. B. & BAEZA-YATES, R. 1992. Information Retrieval. Data
Structure & Algorithms, New York, Prentice Hall.
FU, K. S. 1982. Syntactic pattern recognition and applications, Prentice-Hall
New York.
FUJII, A., TOKUNAGA, T., INUI, K. & TANAKA, H. 1998. Selective
sampling
for
example-based
word
sense
disambiguation.
Computational linguistics, 24, 573-597.
GALE, W. A., CHURCH, K. W. & YAROWSKY, D. One sense per
discourse. Proceedings of the workshop on Speech and Natural
Language, 1992. Association for Computational Linguistics, 233-237.
GALLEY, M. & MCKEOWN, K. Improving word sense disambiguation in
lexical chaining. IJCAI, 2003. 1486-1488.
GIL-GARCÍA, R. J., BADÍA-CONTELLES, J. M. & PONS-PORRATA, A.
2003. Extended star clustering algorithm.
Progress in Pattern
Recognition, Speech and Image Analysis. Springer.
GOLUB, G. H. & VAN LOAN, C. F. 2012. Matrix computations, JHU Press.
GRABOWSKI, A. 2004. Basic properties of Rough Sets and Rough
85
GRAFF, D., KONG, J., CHEN, K. & MAEDA, K. 2003. English gigaword.
Linguistic Data Consortium, Philadelphia.
GRUBER, T. R. 1995. Toward principles for the design of ontologies used
for knowledge sharing?
International journal of human-computer
studies, 43, 907-928.
HALLIDAY, M. A. A. H., R., EDS. 1976. Cohesion in English. Longman
Group Ltd, London, U.K.
HARABAGIU, S., MILLER, G. & MOLDOVAN, D. Wordnet 2-a
morphologically and semantically enhanced resource. Proceedings of
SIGLEX, 1999. 1-8.
HEARST, M. 1991. Noun homograph disambiguation using local context in
large text corpora. Using Corpora, 185-188.
HIRST, G. & ST-ONGE, D. 1998. Lexical chains as representations of
context for the detection and correction of malapropisms. WordNet:
An electronic lexical database, 305, 305-332.
HOSTE, V., HENDRICKX, I., DAELEMANS, W. & VAN DEN BOSCH, A.
2002. Parameter optimization for machine-learning of word sense
disambiguation. Natural Language Engineering, 8, 311-325.
IDE, N., ERJAVEC, T. & TUFIS, D. Automatic Sense Tagging Using
Parallel Corpora. NLPRS, 2001. 83-90.
IDE, N. & SUDERMAN, K. Integrating linguistic resources: The american
national corpus model. Proceedings of the 6th International
Conference on Language Resources and Evaluation, 2006. Citeseer.
IDE, N. & VÉRONIS, J. 1998. Introduction to the special issue on word
sense disambiguation: the state of the art. Computational linguistics,
24, 2-40.
JIANG, J. J. & CONRATH, D. W. 1997. Semantic similarity based on
corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008.
JURAFSKY, D. & MARTIN, J. H. 2000. Speech & Language Processing,
Pearson Education India.
KELLY, E. F. & STONE, P. J. 1975. Computer recognition of English word
86
KILGARRIFF, A. 1997. I don’t believe in word senses. Computers and the
Humanities, 31, 91-113.
KILGARRIFF, A. & GREFENSTETTE, G. 2003. Introduction to the special
issue on the web as corpus. Computational linguistics, 29, 333-347.
KILGARRIFF, A. & YALLOP, C. What's in a Thesaurus? LREC, 2000.
KILGARRIFF, D. A. 2006. Word senses.
Word Sense Disambiguation.
Springer.
KLEIN, D., TOUTANOVA, K., ILHAN, H. T., KAMVAR, S. D. & MANNING,
C. D. Combining heterogeneous classifiers for word-sense
disambiguation. Proceedings of the ACL-02 workshop on Word
sense disambiguation: recent successes and future directions-Volume
8, 2002. Association for Computational Linguistics, 74-80.
KOMOROWSKI, J., PAWLAK, Z. & POLKOWSKI, L. 1999a. Rough sets: a
tutorial.
In: PAL, S. K. & SKOWRON, A. (eds.)
Rough-Fuzzy
Hybridization: A New Trend in Decision Making. Singapore: Springer-
Verlag.
KOMOROWSKI, J., PAWLAK, Z., POLKOWSKI, L. & SKOWRON, A.
1999b. A Rough Set Perspective on Data and Knowledge.
In:
KLÖSGEN, W. & ZYTKOW, J. (eds.) The Handbook of Data Mining
and Knowledge Discovery. Oxford University Press.
KU, H. & FRANCIS, W. N. 1967. Computational Analysis of Present-Day
{A} merican {E} nglish.
LEACOCK, C. & CHODOROW, M. 1998. Combining local context and
WordNet similarity for word sense identification.
WordNet: An
electronic lexical database, 49, 265-283.
LEACOCK, C., TOWELL, G. & VOORHEES, E. Corpus-based statistical
sense resolution. Proceedings of the workshop on Human Language
Technology, 1993. Association for Computational Linguistics, 260-
265.
LEE, Y. K. & NG, H. T. An empirical evaluation of knowledge sources and
learning algorithms for word sense disambiguation. Proceedings of
the ACL-02 conference on Empirical methods in natural language
processing-Volume 10, 2002. Association for Computational
Linguistics, 41-48.
87
LESK, M. Automatic sense disambiguation using machine readable
dictionaries: how to tell a pine cone from an ice cream cone.
Proceedings of the 5th annual international conference on Systems
documentation, 1986. ACM, 24-26.
LIANG, J., SHI, Z. & LI, D. 2003. Applications of inclusion degree in rough
set theory. International Journal of Computational Cognition, 1, 67-78.
LIN, D. An information-theoretic definition of similarity. ICML, 1998. 296-
304.
LIN, D. & PANTEL, P. Discovering word senses from text. In Proceedings
of the 8th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, 2002 Edmonton, Alta., Canada. 613-619.
LITKOWSKI,
K.
2005.
Computational
lexicons
and
dictionaries.
Encyclopedia of Language and Linguistics (2nd ed.). Elsevier
Publishers, Oxford, 83-88.
MAGDALENO, D. 2008.
Refinamiento, evaluación y etiquetamiento de
grupos textuales basados en la teoría de los conjuntos aproximados.,
Universidad Central "Martha Abreu" de las Villas.
MAGNINI, B. & CAVAGLIA, G. Integrating Subject Field Codes into
WordNet. LREC, 2000.
MALLERY, J. C. Thinking about foreign policy: Finding an appropriate role
for artificially intelligent computers. Master's thesis, MIT Political
Science Department, 1988. Citeseer.
MANNING, C. D. & SCHÜTZE, H. 1999. Foundations of statistical natural
language processing, MIT press.
MÀRQUEZ, L., ESCUDERO, G., MARTINEZ, D. & RIGAU, G. 2006.
Supervised corpus-based methods for word sense disambiguation.
Word Sense Disambiguation.
MARTÍN-WANTON, T. & PONS-PORRATA, A. 2007. USANDO
DESAMBIGUACIÓN EN LA DETERMINACIÓN DE LA POLARIDAD
DE LAS OPINIONES.
MIHALCEA, R. Bootstrapping Large Sense Tagged Corpora. LREC, 2002.
MIHALCEA, R. & FARUQUE, E. Senselearner: Minimally supervised word
sense disambiguation for all words in open text. Proceedings of
ACL/SIGLEX Senseval, 2004. 155-158.
88
MILLER, G. A., BECKWITH, R., FELLBAUM, C., GROSS, D. & MILLER, K.
J. 1990. Introduction to wordnet: An on-line lexical database*.
International journal of lexicography, 3, 235-244.
MILLER, G. A., LEACOCK, C., TENGI, R. & BUNKER, R. T. A semantic
concordance. Proceedings of the workshop on Human Language
Technology, 1993. Association for Computational Linguistics, 303-
308.
MOONEY, R. J. Comparative experiments on disambiguating word senses:
An illustration of the role of bias in machine learning. Proceedings of
the Conference on Empirical Methods in Natural Language
Processing, 1996. Philadelphia, PA., 82-91.
MORRIS, J. & HIRST, G. 1991. Lexical cohesion computed by thesaural
relations as an indicator of the structure of text.
Computational
linguistics, 17, 21-48.
MURATA, M., UTIYAMA, M., UCHIMOTO, K., MA, Q. & ISAHARA, H.
Japanese word sense disambiguation using the simple Bayes and
support vector machine methods. The Proceedings of the Second
International Workshop on Evaluating Word Sense Disambiguation
Systems, 2001. Association for Computational Linguistics, 135-138.
NAVIGLI, R. 2006a. Consistent validation of manual and automatic sense
annotations with the aid of semantic
graphs.
NAVIGLI, R. 2006b. Experiments on the validation of sense annotations
assisted by lexical chains.
NAVIGLI, R. 2009. Word sense disambiguation: A survey. ACM Computing
Surveys (CSUR), 41, 10.
NAVIGLI, R., VELARDI, P., CUCHIARELLI, A. & NERI, R. 2005. Evaluation
of OntoLearn, a methodology for automatic learning of domain
ontologies. Ontology Learning from Text: Methods, evaluation and
applications, 123, 92.
NG, H. T. Getting serious about word sense disambiguation. Proceedings
of the ACL SIGLEX Workshop on Tagging Text with Lexical
Semantics: Why, What, and How, 1997. 1-7.
89
NG, H. T. & LEE, H. B. Integrating multiple knowledge sources to
disambiguate
word
sense:
An
exemplar-based
approach.
Proceedings of the 34th annual meeting on Association for
Computational Linguistics, 1996. Association for Computational
Linguistics, 40-47.
NG, V. & CARDIE, C. Weakly supervised natural language learning without
redundant views. Proceedings of the 2003 Conference of the North
American Chapter of the Association for Computational Linguistics on
Human Language Technology-Volume 1, 2003. Association for
Computational Linguistics, 94-101.
NIU, C., LI, W., SRIHARI, R. K. & LI, H. Word independent context pair
classification model for word sense disambiguation. Proceedings of
the Ninth Conference on Computational Natural Language Learning,
2005. Association for Computational Linguistics, 33-39.
PAWLAK, Z. 1982. Rough sets. International Journal of Computer and
Information Sciences, 11, 341-356.
PAWLAK, Z. 1991. Rough Sets: Theoretical Aspects of Reasoning about
Data, Dordrecht, Academic Publishers.
PAWLAK, Z. 1995. Vagueness and uncertainty: a rough set perspective.
Computational Intelligence: an International Journal, 11, 227-232.
PAWLAK, Z., GRZYMALA-BUSSE, J. W., SLOWINSKI, R. & ZIARKO, W.
1995. Rough sets. Communications of the ACM, 38, 89-95.
PAWLAK, Z. & SKOWRON, A. 1994. Rough membership functions. In:
YAGER, R., FEDRIZZI, M. & KACPRZYK, J. (eds.) Advances in the
Dempster-Shafer Theory of Evidence. New York: Wiley.
PEASE, A., NILES, I. & LI, J. The suggested upper merged ontology: A
large ontology for the semantic web and its applications. Working
notes of the AAAI-2002 workshop on ontologies and the semantic
web, 2002.
PEDERSEN, T. 1998. Learning probabilistic models of word sense
disambiguation.
PEDERSEN, T., BANERJEE, S. & PATWARDHAN, S. 2005. Maximizing
semantic relatedness to perform word sense disambiguation.
90
University of Minnesota Supercomputing Institute Research Report
UMSI, 25, 2005.
PEDERSEN, T. & BRUCE, R. Distinguishing word senses in untagged text.
Proceedings of the second conference on empirical methods in
natural language processing, 1997. 197-207.
PEDERSEN, T., PATWARDHAN, S. & MICHELIZZI, J. WordNet::
Similarity: measuring the relatedness of concepts. Demonstration
Papers at HLT-NAACL 2004, 2004. Association for Computational
Linguistics, 38-41.
PÉREZ-SUÁREZ, A. 2008. Algoritmos de agrupamiento para la generación
de cubrimientos en colecciones de documentos., Instituto Nacional de
Astrofísica,Óptica y Electrónica (INAOE).
PHILPOT, A., HOVY, E. & PANTEL, P. The omega ontology. Proceedings,
IJCNLP workshop on Ontologies and Lexical Resources (OntoLex-
05), 2005.
PIANTA, E., BENTIVOGLI, L. & GIRARDI, C. Developing an aligned
multilingual database. Proc. 1st Int’l Conference on Global
WordNet, 2002.
PROCTOR, P., ED. 1978. Longman Dictionary of Contemporary English.
Longman Group, Harlow, U.K.
PURANDARE, A. & PEDERSEN, T. Improving word sense discrimination
with gloss augmented feature vectors. Appears in the Proceedings of
the Workshop on Lexical Resources for the Web and Word Sense
Disambiguation, 2004. Citeseer.
PUSTEJOVSKY, J. 1991. The generative lexicon.
Computational
linguistics, 17, 409-441.
PUSTEJOVSKY, J. 1995. The generative lexicon, 1995. MIT Press,
Cambridge, MA.
RADA, R., MILI, H., BICKNELL, E. & BLETTNER, M. 1989. Development
and application of a metric on semantic nets. Systems, Man and
Cybernetics, IEEE Transactions on, 19, 17-30.
RESNIK, P. 1995. Using information content to evaluate semantic similarity
in a taxonomy. arXiv preprint cmp-lg/9511007.
91
ROGET, P. M. 1911. Roget's Thesaurus of English Words and Phrases, TY
Crowell Company.
SAVOVA, G., PEDERSEN, T., PURANDARE, A. & KULKARNI, A. 2005.
Resolving ambiguities in biomedical text with unsupervised clustering
approaches.
University of Minnesota Supercomputing Institute
Research Report.
SCHUTZE, H. Dimensions of meaning. Supercomputing'92., Proceedings,
1992. IEEE, 787-796.
SCHÜTZE, H. 1998. Automatic word sense discrimination. Computational
linguistics, 24, 97-123.
SILBER, H. G. & MCCOY, K. F. Efficient text summarization using lexical
chains. Proceedings of the 5th international conference on Intelligent
user interfaces, 2000. ACM, 252-255.
SKOWRON, A. Rough sets in KDD. In: SHI, Z., FALTINGS, B. & MUSEN,
M., eds. Proceedings of the Conference on Intelligent Information
Processing (IIP2000), 2000 Beijing. Publishing House of Electronic
Industry, 1-17.
SLOWINSKI, R. & VANDERPOOTEN, D. 1998. Similarity relation as a
basis for rough approximations. In: WANG, P. P. (ed.) Advances in
Machine Intelligence & Soft-Computing. Raleigh, NC: Bookwrights.
SOANES, C. A. S., A., EDS. 2003. Oxford Dictionary of English.
SUÁREZ, A. P. & PAGOLA, J. E. M. A clustering algorithm based on
generalized stars. . In Proceedings of the 5th International
Conference on Machine Learning and Data Mining in Pattern
Recognition, 2007 Leipzig, Germany. 248-262.
SUSSNA, M. Word sense disambiguation for free-text indexing using a
massive semantic network. Proceedings of the second international
conference on Information and knowledge management, 1993. ACM,
67-74.
TOWELL, G. & VOORHEES, E. M. 1998. Disambiguating highly ambiguous
words. Computational linguistics, 24, 125-145.
TSATSARONIS, G., VAZIRGIANNIS, M. & ANDROUTSOPOULOS, I. Word
Sense Disambiguation with Spreading Activation Networks Generated
from Thesauri. IJCAI, 2007. 1725-1730.
92