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)
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
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
Assessing aspects of reading by a connectionist model
Neurocomputing
A connectionist model of human reading
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
| Hi-index | 0.00 |
Traditional document indexing methods, although useful, do not take into account some important aspects of language, such as syntax and semantics. Unlikely, semantic hyperspaces are mathematical and statistical-based techniques that do it. However, although they are an improvement on traditional methods, the output representation is still vector like. This paper proposes a computational model of text reading, called Cognitive Reading Indexing (CRIM), inspired by some aspects of human reading cognition, such as sequential perception, temporality, memory, forgetting and inferences. The model produces not vectors but nets of activated concepts. This paper is focused on indexing or representing documents that way so that they can be labeled or retrieved, presenting promising results. The system was applied to model human subjects as well, and some interesting results were obtained.