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-- AndreyRzhetsky - 25 Aug 2007

Computation Institute Disciplinary Deep Dive on Language and Computation


Language and Computation 3-D Agenda

Topic 1: Text-mining and natural-language processing: computational problems

Computer-aided analysis of natural texts (books, scientific articles, voice recordings) + text-generation.

When we study the literature surrounding a given scientific topic, we typically embark on a series of tasks, each of variable difficulty. We (A) decide what is relevant to the topic, (B) determine where to look for information, (C) identify and capture pertinent statements scattered through volumes of unrelated passages, and (D) synthesize disparate pieces of knowledge into a coherent whole, possibly through resolution of conflicts among myriad noisy statements and between textual and raw experimental data. To achieve a deeper understanding of the literature and the data therein, we may (E) discover novel connections between seemingly unrelated phenomena and generate testable hypotheses. A still higher level of comprehension might include (F) the construction of advanced logical or computational inferences — in much the same way as a mathematician might conceive of a theorem while thinking about corollaries formulated by colleagues.

While many of these individual tasks can be performed at some level by computers, humans remain the main synthesizers of information. We combine the various parts and slices of understanding to generate the big-picture view. However, while the task of extracting multi-level meaning from text passages today remains one that humans do best, recent advances in computational technology suggest that computers may participate too.

Computer scientists tend to assign tasks (A-B) and (C) to the disciplines of information retrieval (IR) and information extraction (IE), respectively (although one can certainly find publications with different definitions). Although multiple definitions do exist, text mining is typically associated with information retrieval, extraction, and synthesis (tasks B-E)—with stress on discovering novel knowledge (E). Note that steps (A-F) are also the bread-and-butter of the artificial intelligence (AI) research community, while (A-C) are also the territory of natural language processing (NLP) and computational linguistics.

Topic 2: Problems in computational linguistics

* NotExistingYet

Topic 3: Origin and evolution of language

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Topic 4: Computational psychology of language

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Topic 5: Language and computational neuroscience

Normal neurobiology of language and the effects of disease on this system.

Topic 6: Language and complexity

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Other Topics, Details TBD

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We are considering a further set of meetings, with the precise topics to be defined.

ThreeD Web Utilities

Topic attachments
I Attachment Action Size Date Who Comment
JPEGjpg codex.jpg manage 51.7 K 2007-08-25 - 01:01 AndreyRzhetsky  
Topic revision: r36 - 2013-08-07 - DavidEForero
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