A Cognitive Approach to Word Sense Disambiguation

An unsupervised, knowledge-based, parametric approach to Word Sense Disambiguation is proposed based on the well-known cognitive architecture ACT-R. In this work, the target word is disambiguated based on surrounding context words using an accumulator model of memory search and it is realized by incorporating RACE/A with ACT-R 6.0. In this process, a spreading activation network is built following the strategies of Tsatsaronis et al. proposed in [5] using the chunks and their relations in the declarative memory system of ACT-R and the lexical representation has been achieved by integrating WordNet with the cognitive architecture. The resulting Word Sense Disambiguation system is evaluated using the test data set from English Lexical Sample task of Senseval-2 and overall accuracy of the proposed algorithm is 44.74% which outperforms all the participating Word Sense Disambiguation Systems.

[1]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[2]  John R. Anderson.  A spreading activation theory of memory. , 1983 .

[3]  Scott Cotton,et al.  SENSEVAL-2: Overview , 2001, *SEMEVAL.

[4]  Nancy Ide,et al.  Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art , 1998, Comput. Linguistics.

[5]  Helmut Berger,et al.  An Adaptive Information Retrieval System Based on Associative Networks , 2004, APCCM.

[6]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[7]  Marvin Minsky,et al.  Semantic Information Processing , 1968 .

[8]  Michalis Vazirgiannis,et al.  Word Sense Disambiguation with Spreading Activation Networks Generated from Thesauri , 2007, IJCAI.

[9]  M R Quillian,et al.  Word concepts: a theory and simulation of some basic semantic capabilities. , 1967, Behavioral science.

[10]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[11]  M. Ross Quillian,et al.  The teachable language comprehender: a simulation program and theory of language , 1969, CACM.

[12]  Ted Pedersen,et al.  The Senseval-3 Multilingual English-­Hindi lexical sample task , 2004, SENSEVAL@ACL.

[13]  Michael E. Lesk,et al.  Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone , 1986, SIGDOC '86.

[14]  Felix Naumann,et al.  Data fusion , 2009, CSUR.

[15]  Roberto Navigli,et al.  Word sense disambiguation: A survey , 2009, CSUR.

[16]  Leendert van Maanen,et al.  Context effects on memory retrieval: Theory and applications , 2009 .

[17]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[18]  D. H. Van Rijn,et al.  RACE for retrieval: Competitive effects in memory retrieval , 2005 .

[19]  John R. Anderson.  Language, Memory, and Thought , 1976 .

[20]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[21]  Ted Pedersen,et al.  An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet , 2002, CICLing.

[22]  R. Schvaneveldt,et al.  Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations. , 1971, Journal of experimental psychology.

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

[24]  Nancy Ide,et al.  Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries , 1990, COLING.