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<title>Natural Language Acquisition</title>

<h1>Natural Language Acquisition</h1>

To view a paper, click on the open book image. <br> 
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<a name="chill-ilp-96.ps.Z"</a>

<b><li>Inductive Logic Programming for Natural Language Processing</b>
Raymond J. Mooney<br>

<cite>Proceedings of the 6th International Inductive Logic Programming Workshop</cite>, pp. 205-224, Stockholm, Sweden, August 1996. <p>

<blockquote>
This paper reviews our recent work on applying inductive logic
programming to the construction of natural language processing
systems. We have developed a system, CHILL, that learns a parser from
a training corpus of parsed sentences by inducing heuristics that
control an initial overly-general shift-reduce parser.  CHILL learns
syntactic parsers as well as ones that translate English database
queries directly into executable logical form.  The ATIS corpus of
airline information queries was used to test the acquisition of
syntactic parsers, and CHILL performed competitively with recent
statistical methods.  English queries to a small database on
U.S. geography were used to test the acquisition of a complete natural
language interface, and the parser that CHILL acquired was more
accurate than an existing hand-coded system.  The paper also includes
a discussion of several issues this work has raised regarding the
capabilities and testing of ILP systems as well as a summary of our
current research directions.
</blockquote>

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<b><li>Comparative Experiments on Disambiguating Word Senses:
An Illustration of the Role of Bias in Machine Learning
<br></b>
Raymond J. Mooney<br>

<cite>Proceedings of the 1996 Conference on Empirical Methods in
Natural Language Processing</cite>, pp. 82-91, Philadelphia, PA, May
1996.. <p>

<blockquote>
This paper describes an experimental comparison of seven different learning
algorithms on the problem of learning to disambiguate the meaning of a word
from context.  The algorithms tested include statistical, neural-network,
decision-tree, rule-based, and case-based classification techniques.  The
specific problem tested involves disambiguating six senses of the word ``line''
using the words in the current and proceeding sentence as context.  The
statistical and neural-network methods perform the best on this particular
problem and we discuss a potential reason for this observed difference.  We
also discuss the role of bias in machine learning and its importance in
explaining performance differences observed on specific problems.
</blockquote>

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<b><li>Learning to Parse Database Queries using Inductive Logic Programming<br></b>
John M. Zelle and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 1050-1055, Portland, OR, August, 1996. (AAAI-96)<p>


<blockquote>
This paper presents recent work using the CHILL parser acquisition
system to automate the construction of a natural-language interface
for database queries.  CHILL treats parser acquisition as the learning
of search-control rules within a logic program representing a
shift-reduce parser and uses techniques from Inductive Logic
Programming to learn relational control knowledge.  Starting with a
general framework for constructing a suitable logical form, CHILL is
able to train on a corpus comprising sentences paired with database
queries and induce parsers that map subsequent sentences directly into
executable queries.  Experimental results with a complete
database-query application for U.S. geography show that CHILL is able
to learn parsers that outperform a pre-existing, hand-crafted
counterpart.  These results demonstrate the ability of a corpus-based
system to produce more than purely syntactic representations.  They
also provide direct evidence of the utility of an empirical approach
at the level of a complete natural language application.
</blockquote>

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<b><li>Lexical Acquisition: A Novel Machine Learning Problem<br></b>
Cynthia A. Thompson and Raymond J. Mooney<br>

Technical Report, Artificial Intelligence Lab, 
University of Texas at Austin, 1996. <p>

<blockquote>
This paper defines a new machine learning problem to which standard
machine learning algorithms cannot easily be applied.  The problem
occurs in the domain of lexical acquisition. The ambiguous and
synonymous nature of words causes the difficulty of using standard
induction techniques to learn a lexicon.  Additionally, negative
examples are typically unavailable or difficult to construct in this
domain.  One approach to solve the lexical acquisition problem is
presented, along with preliminary experimental results on an
artificial corpus.  Future work includes extending the algorithm and
performing tests on a more realistic corpus.
</blockquote>

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<b><li>Learning Parse Decisions From Examples With Rich Context<br></b>
Ulf Hermjakob and Raymond J. Mooney<br>

Submitted to the 34th Annual Meeting of the Association for Computational Linguistics (ACL-96).<p>

<blockquote>
We present a knowledge and context-based system for parsing natural language
and evaluate it on sentences from the Wall Street Journal.
Applying machine learning techniques, the system uses parse action examples
acquired under supervision to generate a deterministic shift-reduce parser
in the form of a decision structure. It relies heavily on context, as encoded 
in features which describe the morpholgical, syntactical, semantical and
other aspects of a given parse state.
</blockquote>

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<b><li> Corpus-Based Lexical Acquisition For Semantic Parsing <br></b>
Cynthia Thompson<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1995. <p>

<blockquote>
Building accurate and efficient natural language processing (NLP)
systems is an important and difficult problem.  There has been
increasing interest in automating this process.  The lexicon, or the
mapping from words to meanings, is one component that is typically
difficult to update and that changes from one domain to the next.
Therefore, automating the acquisition of the lexicon is an important
task in automating the acquisition of NLP systems.  This proposal
describes a system, WOLFIE (WOrd Learning From Interpreted Examples),
that learns a lexicon from input consisting of sentences paired with
representations of their meanings.  Preliminary experimental results
show that this system can learn correct and useful mappings.  The
correctness is evaluated by comparing a known lexicon to one learned
from the training input.  The usefulness is evaluated by examining the
effect of using the lexicon learned by WOLFIE to assist a parser
acquisition system, where previously this lexicon had to be
hand-built.  Future work in the form of extensions to the algorithm,
further evaluation, and possible applications is discussed.
</blockquote>

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

<b><li>Comparative Results on Using Inductive Logic Programming
for Corpus-based Parser Construction<br></b>
John M. Zelle and Raymond J. Mooney<br>

<cite>Symbolic, Connectionist, and Statistical Approaches to Learning for Natural
Language Processing</cite>, S. Wermter, E. Riloff and G. Scheler, Eds,
Spring Verlag, 1996. <p> 

<blockquote>
This paper presents results from recent experimenets with CHILL, a
corpus-based parser acquisition system.  CHILL treats language
acquisition as the learning of search-control rules within a logic
program.  Unlike many current corpus-based approaches that use
statistical learning algorithms, CHILL uses techniques from inductive
logic programming (ILP) to learn relational representations.  CHILL is
a very flexible system and has been used to learn parsers that produce
syntactic parse trees, case-role analyses, and executable database
queries.  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 statistical methods and that the control-rule framework
is fundamental to CHILL's success.
</blockquote>

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<b><li>Learning the Past Tense of English Verbs Using Inductive Logic 
Programming<br></b>
Raymond J. Mooney and Mary Elaine Califf<br>

<cite>Symbolic, Connectionist, and Statistical Approaches to Learning for Natural
Language Processing</cite>, S. Wermter, E. Riloff and G. Scheler, Eds,
Spring Verlag, 1996. <p> 

<blockquote> 

This paper presents results on using a new inductive logic programming
method called FOIDL to learn the past tense of English verbs. The past
tense task has been widely studied in the context of the
symbolic/connectionist debate.  Previous papers have presented results
using various neural-network and decision-tree learning methods.  We
have developed a technique for learning a special type of Prolog
program called a <em>first-order decision list</em>, defined as an
ordered list of clauses each ending in a cut.  FOIDL is based on FOIL
(Quinlan, 1990) 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 the past-tense task.  We present results showing that FOIDL learns
a more accurate past-tense generator from significantly fewer examples
than all other previous methods.
</blockquote>

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

<b><li>Using Inductive Logic Programming to Automate the Construction of 
Natural Language Parsers<br></b>

John M. Zelle<br>

Ph.D. Thesis, Deparment of Computer Sciences, University of Texas at Austin, August, 1995.<p>


<blockquote>
Designing computer systems to understand natural language input is a
difficult task.  In recent years there has been considerable interest
in corpus-based methods for constructing natural language parsers.
These empirical approaches replace hand-crafted grammars with
linguistic models acquired through automated training over language
corpora.  A common thread among such methods to date is the use of
propositional or probablistic representations for the learned
knowledge.  This dissertation presents an alternative approach based
on techniques from a subfield of machine learning known as inductive