J Biomed Inform - Unsupervised grammar induction and similarity retrieval in medical language processing using the Deterministic Dynamic Associative Memory (DDAM) model.

Tópicos

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{ studi(2440) review(1878) systemat(933) }
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Resumo

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.

Resumo Limpo

paper overview unsupervis grammar induct similar retriev two fundament inform process function import medic languag process applic construct intellig medic inform system exist literatur focus text segment task review review includ comparison exist approach reveal longstand interest tradit distinct topic despit signific comput challeng character unifi approach unsupervis represent process sequenti data determinist dynam associ memori ddam model introduc describ theoret structur function perspect theoret descript model complement select discuss interest experiment result task unsupervis grammar induct similar retriev applic medic languag process notwithstand challeng associ evalu unsupervis informationprocess model conclud ddam model demonstr interest properti encourag investig theoret appli context

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