文档介绍：NATURAL LANGUAGE INFERENCE
A DISSERTATION
SUBMITTED TO THE DEPARTMENT PUTER SCIENCE
AND MITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Bill artney
June 2009
c Copyright by Bill artney 2009
All Rights Reserved
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I certify that I have read this dissertation and that, in my opinion, it
is fully adequate in scope and quality as a dissertation for the degree
of Doctor of Philosophy.
(Christopher D. Manning) Principal Adviser
I certify that I have read this dissertation and that, in my opinion, it
is fully adequate in scope and quality as a dissertation for the degree
of Doctor of Philosophy.
(Dan Jurafsky)
I certify that I have read this dissertation and that, in my opinion, it
is fully adequate in scope and quality as a dissertation for the degree
of Doctor of Philosophy.
(Stanley Peters)
Approved for the mittee on Graduate Studies.
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Abstract
Inference has been a central topic in artificial intelligence from the start, but while
automatic methods for formal deduction have advanced tremendously, comparatively
little progress has been made on the problem of natural language inference (NLI),
that is, determining whether a natural language hypothesis h can justifiably be in-
ferred from a natural language premise p. The challenges of NLI are quite different
from those encountered in formal deduction: the emphasis is on informal reasoning,
lexical semantic knowledge, and variability of linguistic expression. This dissertation
explores a range of approaches to NLI, beginning with methods which are robust but
approximate, and proceeding to progressively more precise approaches.
We first develop a baseline system based on overlap between bags of words. Despite
its extreme simplicity, this model achieves surprisingly good results on a standard NLI
evaluation, the PASCAL RTE Challenge. However, its effectiveness is limited by its
failure to re