Posts Tagged ‘natural language’

1 Jan

A Theory of Questions and Question Asking

Posted by cognitivecomputing in Language, Learning. Tagged: , , .

This article focusses on knowledge goals, that is, the goals of a reasoner to acquire or reorganize knowledge. Knowledge goals, often expressed as questions, arise when the reasoner’s model of the domain is inadequate in some reasoning situation. This leads the reasoner to focus on the knowledge it needs, to formulate questions to acquire this knowledge, and to learn by pursuing its questions. I develop a theory of questions and of question-asking, motivated both by cognitive and computational considerations, and I discuss the theory in the context of the task of story understanding. I present a computer model of an active reader that learns about novel domains by reading newspaper stories.

Read the paper:

A Theory of Questions and Question Asking

by Ashwin Ram

The Journal of the Learning Sciences, 1(3&4):273-318, 1991
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-02.pdf

27 Jul

Decision Models: A Theory of Volitional Explanation

Posted by cognitivecomputing in Language. Tagged: , , , , .

This paper presents a theory of motivational analysis, the construction of volitional explanations to describe the planning behavior of agents. We discuss both the content of such explanations as well as the process by which an understander builds the explanations. Explanations are constructed from decision models, which describe the planning process that an agent goes through when considering whether to perform an action. Decision models are represented as explanations patterns, which are standard patterns of causality based on previous experiences of the understander. We discuss the nature of explanation patterns, their use in representing decision models, and the process by which they are retrieved, used, and evaluated.

Read the paper:

Decision Models: A Theory of Volitional Explanation

by Ashwin Ram

Twelvth Annual Conference of the Cognitive Science Society (CogSci-90), Cambridge, MA, July 1990
www.cc.gatech.edu/faculty/ashwin/papers/er-90-03.pdf

27 Jul

Knowledge Goals: A Theory of Interestingness

Posted by cognitivecomputing in Language. Tagged: , , .

Combinatorial explosion of inferences has always been one of the classic problems in AI. Resources are limited, and inferences potentially infinite; a reasoner needs to be able to determine which inferences are useful to draw from a given piece of text. But unless one considers the goals of the reasoner, it is very difficult to give a principled definition of what it means for an inference to be “useful.”

This paper presents a theory of inference control based on the notion of interestingness. We introduce knowledge goals, the goals of a reasoner to acquire some piece of knowledge required for a reasoning task, as the focusing criteria for inference control. We argue that knowledge goals correspond to the interests of the reasoner, and present a theory of interestingness that is functionally motivated by consideration of the needs of the reasoner. Although we use story understanding as the reasoning task, many of the arguments carry over to other cognitive tasks as well.

Read the paper:

Knowledge Goals: A Theory of Interestingness

by Ashwin Ram

Twelvth Annual Conference of the Cognitive Science Society, 206-214, Cambridge, MA, July 1990
www.cc.gatech.edu/faculty/ashwin/papers/er-90-02.pdf

1 Sep

Evaluating Text-Mining Strategies for Interpreting DNA Microarray Expression Profiles

Posted by cognitivecomputing in Health & Wellness, Language, Learning. Tagged: , , .

To facilitate the interpretation of large data sets generated by DNA microarray studies, we are 1) developing a text mining system to extract keywords from MEDLINE abstracts associated with individual gene names and 2) investigating several clustering algorithms to determine relationships between genes based on shared keywords. The basic mechanisms of our keyword extraction algorithm was described previously (Soc Neurosci Abstr 2001, 557.4). Recent progress in evaluating the performance of this algorithm through Precision-Recall calculations and in using extracted keywords to accurately cluster predefined groups of genes are reported here.

Evaluating Text-Mining Strategies for Interpreting DNA Microarray Expression Profiles

by Brian Ciliax, Ying Liu, Jorge Civera, Ashwin Ram, Sham Navathe, Ray Dingledine

Annual Meeting of the Society for Neuroscience (Soc Neurosci Abstr), Orlando, FL, September 2002
www.cc.gatech.edu/faculty/ashwin/papers/er-02-01.pdf
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