http:^^www.cs.utexas.edu^users^ml^ilp.html

This data set contains WWW-pages collected from computer science departments of various universities

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<a name="chill-compling-95.ps.Z"</a>

<b><li>An Inductive Logic Programming Method for Corpus-based Parser
Construction<br></b>

John M. Zelle and Raymond J. Mooney<br>

Submitted to Computational Linguistics<p>

<blockquote>
In recent years there has been considerable research into corpus-based
methods for parser construction.  A common thread in this research has
been the use of propositional representations for learned knowledge.
This paper presents an alternative approach based on techniques from a
subfield of machine learning known as inductive logic programming
(ILP).  ILP, which investigates the learning of relational
(first-order) rules, provides a way of using empricial methods to
acquire knowledge within traditional, symbolic parsing frameworks.  We
describe a novel method for constructing deterministic Prolog parsers
from corpora of parsed sentences.  We also discuss several advantages
of this approach compared to propositional alternatives and present
experimental results on learning complete parsers using several
corpora including the ATIS corpus from the Penn Treebank.
</blockquote>

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<a name="chill-ijcai-nll-95.ps.Z"</a>

<b><li>A Comparison of Two Methods Employing Inductive Logic Programming for
Corpus-based Parser Constuction<br></b>

John M. Zelle and Raymond J. Mooney<br>

<cite>Working Notes of the IJCAI-95 Workshop on New Approaches to
Learning for Natural Language Processing</cite>, pp.79-86, Montreal,
Quebec, August, 1995.<p>

<blockquote> 
This paper presents results from recent experiments with CHILL,
a corpus-based parser acquisition system.  CHILL treats grammar
acquisition as the learning of search-control rules within a logic
program.  Unlike many current corpus-based approaches that use
propositional or probabilistic learning algorithms, CHILL uses
techniques from inductive logic programming (ILP) to learn relational
representations.  The reported experiments compare CHILL's
performance to that of a more naive application of ILP to parser
acquisition.  The results show that ILP techniques, as employed in
CHILL, are a viable alternative to propositional methods and
that the control-rule framework is fundamental to CHILL's
success.
</blockquote>

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<a name="chill-ifoil-ml-95.ps.Z"</a>

<b><li>Inducing Logic Programs without Explicit Negative Examples<br></b>

John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney<br>

<cite>Proceedings of the Fifth International Workshop on Inductive Logic
Programming</cite>, Leuven, Belguim, Sepetember 1995.<p>


<blockquote> This paper presents a method for learning logic programs
without explicit negative examples by exploiting an assumption of
<i>output completeness</i>. A mode declaration is supplied for the
target predicate and each training input is assumed to be accompanied
by all of its legal outputs.  Any other outputs generated by an
incomplete program implicitly represent negative examples; however,
large numbers of ground negative examples never need to be generated.
This method has been incorporated into two ILP systems, CHILLIN and
IFOIL, both of which use intensional background knowledge.  Tests on
two natural language acquisition tasks, case-role mapping and
past-tense learning, illustrate the advantages of the approach.
</blockquote>

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<a name="foidl-jair-95.ps.Z"</a>

<b><li>Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs<br></b>

Raymond J. Mooney and Mary Elaine Califf <br>

<cite>Journal of Artificial Intelligence Research</cite>, 3 (1995) pp. 1-24.

<blockquote>
This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined
as ordered lists of clauses each ending in a cut.  The method, called FOIDL, 
is based on FOIL but employs intensional background knowledge and avoids 
the need for explicit negative examples.  It is particularly useful for 
problems that involve rules with specific exceptions, such as learning the 
past-tense of English verbs, a task widely studied in the context of the 
symbolic/connectionist debate.  FOIDL is able to learn concise, accurate 
programs for this problem from significantly fewer examples than previous 
methods (both connectionist and symbolic).
</blockquote>

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<a name="chill-ml-94.ps.Z" </a>

<li> <b> Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming
</b> <br>
John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser <br>

<cite> Proceedings of the Eleventh International Workshop
on Machine Learning</cite>, pp. 343-351, Rutgers, NJ, July 1994. (ML-94) <p>

<blockquote>
This paper describes a new method for inducing logic programs from
examples which attempts to integrate the best aspects of existing ILP
methods into a single coherent framework.  In particular, it combines
a bottom-up method similar to GOLEM with a top-down method similar to
FOIL.  It also includes a method for predicate invention similar to
CHAMP and an elegant solution to the ``noisy oracle'' problem which
allows the system to learn recursive programs without requiring a
complete set of positive examples.  Systematic experimental
comparisons to both GOLEM and FOIL on a range of problems are used to
clearly demonstrate the advantages of the approach.
</blockquote>

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<a name="chill-aaai-94.ps.Z" </a>

<b> <li> Inducing Deterministic Prolog Parsers From Treebanks:
A Machine Learning Approach </b> <br>  

John M. Zelle and Raymond J. Mooney <br> 
<cite> Proceedings of the Twelfth National Conference on
AI</cite>, pp. 748-753, Seattle, WA, July 1994. (AAAI-94) <p>
 
<blockquote>
This paper presents a method for constructing deterministic, context-sensitive,
Prolog parsers from corpora of parsed sentences. Our approach uses recent
machine learning methods for inducing Prolog rules from examples (inductive
logic programming).  We discuss several advantages of this method compared to
recent statistical methods and present results on learning complete parsers
from portions of the ATIS corpus.
</blockquote>

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<a name="ilp-ebl-sigart-94.ps.Z" </a>

<b> <li> Integrating ILP and EBL </b> <br>  

Raymond J. Mooney and John M. Zelle <br> 

<cite> SIGART Bulletin</cite>, Volume 5, number 1, Jan. 1994, pp 12-21. <p>

<blockquote>
This paper presents a review of recent work that integrates methods from
Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL).  ILP
and EBL methods have complementary strengths and weaknesses and a number of
recent projects have effectively combined them into systems with better
performance than either of the individual approaches. In particular, integrated
systems have been developed for guiding induction with prior knowledge
(ML-SMART, FOCL, GRENDEL) refining imperfect domain theories
(FORTE, AUDREY, Rx), and learning effective search-control
knowledge (AxA-EBL, DOLPHIN).
</blockquote>

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<b> <li> Combining FOIL and EBG to Speed-Up Logic Programs </b> <br> 

John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence</cite>, pp. 1106-1111, Chambery, France, 1993. (IJCAI-93) <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When
these control rules are incorporated into the original program,
significant speed-up may be achieved.  The algorithm is shown to be an
improvement over competing EBL approaches in several domains.
Additionally, the algorithm is capable of automatically transforming
some intractable algorithms into ones that run in polynomial time.
</blockquote>

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<b> <li> Learning Semantic Grammars With Constructive Inductive Logic Programming </b> <br> 

John M. Zelle  and Raymond J. Mooney <br> 

<cite> Proceedings of the Eleventh National Conference of the American
Association for Artificial Intelligence</cite>, pp. 817-822,
Washington, D.C. July 1993 (AAAI-93). <p>

<blockquote>
Automating the construction of semantic grammars is a difficult and
interesting problem for machine learning.  This paper shows how the
semantic-grammar acquisition problem can be viewed as the learning of
search-control heuristics in a logic program.  Appropriate control
rules are learned using a new first-order induction algorithm that
automatically invents useful syntactic and semantic categories.
Empirical results show that the learned parsers generalize well to
novel sentences and out-perform previous approaches based on
connectionist techniques.
</blockquote>

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<b> <li> Speeding-up Logic Programs by Combining EBG and FOIL </b> <br> 
 
John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the 1992 Machine Learning Workshop on Knowledge
Compilation and Speedup Learning</cite>, Aberdeen Scotland, July 1992. <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When these
control rules are incorporated into the original program, significant
speed-up may be achieved.  The algorithm produces not only EBL-like
speed up of problem solvers, but is capable of automatically
transforming some intractable algorithms into ones that run in
polynomial time.
</blockquote>

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<b> <li> Refinement of First-Order Horn-Clause Domain Theories </b> <br>

Bradley L. Richards and Raymond J. Mooney <br>

<cite> Machine Learning</cite> 19,2 (1995), pp. 95-131. <p>

<blockquote> Knowledge acquisition is a difficult and time-consuming
task, and as error-prone as any human activity.  The task of
automatically improving an existing knowledge base using learning
methods is addressed by a new class of systems performing <i> theory
refinement</i>.  Until recently, such systems were limited to
propositional theories.  This paper presents a system, FORTE
(First-Order Revision of Theories from Examples), for refining
first-order Horn-clause theories.  Moving to a first-order
representation opens many new problem areas, such as logic program
debugging and qualitative modelling, that are beyond the reach of
propositional systems.  FORTE uses a hill-climbing approach to revise
theories.  It identifies possible errors in the theory and calls on a
library of operators to develop possible revisions.  The best revision
is implemented, and the process repeats until no further revisions are
possible.  Operators are drawn from a variety of sources, including
propositional theory refinement, first-order induction, and inverse
resolution.  FORTE has been tested in several domains including
logic programming and qualitative modelling.  </blockquote>

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<address><!WA38><a href="http://www.cs.utexas.edu/users/estlin/">[e-mail]</a></address>