An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
A bijection on Dyck paths and its consequences
Discrete Mathematics
An Efficient, Probabilistically Sound Algorithm for Segmentation andWord Discovery
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Sparse Distributed Memory
Artificial Intelligence Review
A statistical model for word discovery in transcribed speech
Computational Linguistics
Unsupervised learning of the morphology of a natural language
Computational Linguistics
Chinese text segmentation with MBDP-1: making the most of training corpora
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Knowledge-free induction of morphology using latent semantic analysis
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Biomedical text retrieval in languages with a complex morphology
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Unsupervised discovery of morphemes
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Bootstrapping lexical choice via multiple-sequence alignment
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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This paper is an overview of unsupervised grammar induction and similarity retrieval, two fundamental information processing functions of importance to medical language processing applications and to the construction of intelligent medical information systems. Existing literature with a focus on text segmentation tasks is reviewed. The review includes a comparison of existing approaches and reveals the longstanding interest in these traditionally distinct topics despite the significant computational challenges that characterizes them. Further, a unifying approach to unsupervised representation and processing of sequential data, the Deterministic Dynamic Associative Memory (DDAM) model, is introduced and described theoretically from both structural and functional perspectives. The theoretical descriptions of the model are complemented by a selection and discussion of interesting experimental results in the tasks of unsupervised grammar induction and similarity retrieval with applications to medical language processing. Notwithstanding the challenges associated with the evaluation of unsupervised information-processing models, it is concluded that the DDAM model demonstrates interesting properties that encourage further investigations in both theoretical and applied contexts.