Lappin, Shalom

English

SAS-SPACE

A generative constituent-context model for improved grammar induction,

A simple rule-based part of speech tagger,

A simple, fast, and effective rule learner,

A stochastic parts program and noun phrase parser for unrestricted text,

A Theory of Language and Information: A Mathematical Approach,

and Clark, E.(2003), Adult reformulations of child errors as negative evidence,

Aspects of the Theory of Syntax,

C4.5: Programs for Machine Learning,

Computation with probes and goals: A parsing perspective,

Computational Properties of Principled-Based Grammatical Theories,

Corpus-based induction of syntactic structure: Models of dependency and constituency,

Current practice in part of speech tagging and suggestions for the future,

Distributional part-of-speech tagging,

Empirical assessment of stimulus poverty arguments,

Formal grammar and information theory: Together again?,

Generative models for statistical parsing with combinatory categorial grammar,

Head-Driven Statistical Models for Natural Language Parsing, PhD thesis,

Inducing syntactic categories by context distribution clustering,

Knowledge of Language: Its Nature, Origin, and Use, Praeger,

Knowledge-free induction of inflectional morphologies,

Learning theories from text,

Machine Learning and the Cognitive Basis of

New Horizons in the Study of Language and Mind,

Parsing the WSJ using CCG and log-linear models,

Some advances in transformation-based part of speech tagging,

Statistical parsing with context-free grammar and word statistics,

Structural Linguistics,

Syntactic Structures,

The Minimalist Program,

The population frequencies of species and the estimation of population parameters,

Unsupervised induction of stochastic context-free grammars using distributional clustering,

Unsupervised learning of the morphology of a natural language,

Using machine learning for nonsentential utterance classification,

Using machine learning techniques for VPE detection,

Verb phrase ellipsis detection using automatically parsed text,

Widecoverage semantic representations from a CCG parser,

Machine Learning and the Cognitive Basis of Natural Language

Machine learning and statistical methods have yielded impressive results in a wide variety of
natural language processing tasks. These advances have generally been regarded as engineering
achievements. In fact it is possible to argue that the success of machine learning methods is
significant for our understanding of the cognitive basis of language acquisition and processing.
Recent work in unsupervised grammar induction is particularly relevant to this issue. It suggests
that knowledge of language can be achieved through general learning procedures, and that a
richly articulated language faculty is not required to explain its acquisition

Utrecht University Repository

Machine Learning and the Cognitive Basis of Natural Language

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Utrecht University Repository