AIC Seminar Series
Using Analogy to Acquire Knowledge from Human Contributors
|Timothy Chklovski||Massachusetts Institute of Technology||[Home Page]|
Date: Wednesday March 12, 2003 at 16:15
Location: EJ228 (Directions)
This talk describes an approach to capturing commonsense
knowledge about objects in their properties from non-expert
human contributors. Capturing such knowledge will clear the
path to more intelligent human-computer interfaces and pave
the way for computers to reason about our world.
In the domain of natural language processing, it will provide the
world knowledge much needed for semantic processing of natural
In this talk, I will introduce _cumulative analogy_, a class of
nearest-neighbor based analogical reasoning algorithms; I will
also present theoretical and empirical results about its
effectiveness. Finally, I will discuss LEARNER, a deployed
system that implements cumulative analogy to collect knowledge
(a live system is available at http://teach-computers.org)
Specifically, Learner acquires assertion-level knowledge by
constructing shallow semantic analogies between a KA topic and
its nearest neighbors and then posing these analogies as natural
language questions to human contributors.
Suppose, for example, that based on the knowledge about
``newspapers'' already present in the knowledge base,
Learner judges ``newspaper'' to be similar to ``book'' and
Further suppose that assertions ``books contain information'' and
``magazines contain information'' are also already in the knowledge
base. Then Learner will use cumulative analogy from the similar
topics to ask humans whether ``newspapers contain information.''
Because similarity between topics is computed based on what is already
known about them, Learner exhibits _bootstrapping_ behavior --- the
quality of its questions improves as it gathers more knowledge.
By summing evidence for and against posing any given question, Learner
(and cumulative analogy) also exhibits _noise tolerance_, limiting the
effect of incorrect similarities.
Empirically, evaluating the percentages of questions answered
affirmatively, negatively and judged to be nonsensical in the
cumulative analogy case compares favorably with the baseline,
no-similarity case that relies on random objects rather than
nearest neighbors. Of the questions
generated by cumulative analogy, contributors answered
45% affirmatively, 28% negatively and marked 13% as nonsensical;
in the control, no-similarity case 8% of questions were answered
affirmatively, 60% negatively and 26% were marked as nonsensical.
The central finding reported in the talk is the knowledge acquisition
power of shallow semantic analogy from nearest neighbors.
Bio for Timothy Chklovski||
Dr. Timothy Chklovski has joined MIT as an undergraduate in CS and
Math in 1994. After a 2.5 years, Dr. Chklovski has continued at MIT
as a graduate student in CS. In 1999, he took a leave of absence to
found and run aQuery, an NLP document understanding and IR company
financed and mentored by Mitchell Kapor and Accel partners. At aQuery,
Dr. Chklovski has developed a number of advanced NLP based search
engine, text mining, and document understanding technologies. In 2001,
Dr. Chklovski has returned to MIT to carry out his doctoral research.
Dr. Chklovski holds PhD, Master's and Bachelor's degrees in Computer
science, as well as a Bachelor's degree in Math, all from MIT.
Russian-born Dr. Chklovski has immigrated to the United States and
learned English at the age of 13. From childhood, he has been
interested in Artificial Intelligence and problem solving; in '93, his
interest in problem solving and mathematics has led him to represent
the United States on a team of six at the International Math Olympiad
in Istanbul, Turkey. His current interests include knowledge
acquisition & representation and natural language processing (esp.
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