A computational study of efficient shortest path algorithms
Computers and Operations Research
Analogy-making as perception: a computer model
Analogy-making as perception: a computer model
Understanding language understanding: computational models of reading
Understanding language understanding: computational models of reading
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Text Classification by Combining Grouping, LSA and kNN
ICIS-COMSAR '06 Proceedings of the 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS International Workshop on Component-Based Software Engineering,Software Architecture and Reuse
Modelling the Stroop effect: A connectionist approach
Neurocomputing
Dynamic memories: analysis of an integrated comprehension and episodic memory retrieval model
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
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Although machines perform much better than human beings in most of the tasks, it is not the case of natural language processing. Computational linguistic systems usually rely on mathematical and statistical formalisms, which are efficient and useful but far from human procedures and therefore not so skilled. This paper proposes a computational model of natural language reading, called Cognitive Reading Indexing Model (CRIM), inspired by some aspects of human cognition, that tries to become as more psychologically plausible as possible. The model relies on a semantic neural network and it produces not vectors but nets of activated concepts as text representations. Based on these representations, measures of semantic similarity are also defined. Human comparison results show that the system is suitable to model human reading. Additional results also point out that the system could be used in real applications concerning natural language processing tasks.