In human sentence processing, cognitive load can be defined many ways. This report considers a definition of cognitive load in terms of the total probability of structural options that have been disconfirmed at some point in a sentence: the surprisal of word w i given its prefix w 0...i-1 on a phrase-structural language model. These loads can be efficiently calculated using a probabilistic Earley parser (Stolcke, 1995) which is interpreted as generating predictions about reading time on a word-by-word basis. Under grammatical assumptions supported by corpusfrequency data, the operation of Stolcke's probabilistic Earley parser correctly predicts processing phenomena associated with garden path structural ambiguity and with the subject/object relative asymmetry.

Most recent research in learning approaches to natural language have studied fairly "low-level" tasks such as morphology, part-of-speech tagging, and syntactic parsing. However, I believe that logical approaches may have the most relevance and impact at the level of semantic interpretation, where a logical representation of sentence meaning is important and useful. We have explored the use of inductive logic programming for learning parsers that map naturallanguage database queries into executable logical form. This work goes against the growing trend in computational linguistics of focusing on shallow but broad-coverage natural language tasks ("scaling up by dumbing down") and instead concerns using logic-based learning to develop narrower, domain-specific systems that perform relatively deep processing. I first present a historical view of the shifting emphasis of research on various tasks in natural language processing and then briefly review our own work on learning for semantic interpretation. I will then attempt to encourage others to study such problems and explain why I believe logical approaches have the most to offer at the level of producing semantic interpretations of complete sentences.

Introduction: Machine Learning and Natural Language CLAIRE CARDIE cardie@cs.cornell.edu Department of Computer Science, Cornell University, Ithaca, NY 14853-7501 RAYMOND J. MOONEY mooney@cs.utexas.edu Department of Computer Sciences, Taylor Hall 2.124, University of Texas, Austin, TX 787121188 The application of machine learning techniques to natural language processing (NLP) has increased dramatically in recent years under the name of "corpus-based," "statistical," or "empirical" methods. However, most of this research has been conducted outside the traditional machine learning research community. This special issue attempts to bridge this divide by assembling an interesting variety of recent research papers on various aspects of natural language learning -- many from authors who do not generally publish in the traditional machine learning literature -- and presenting them to the readers of Machine Learning. In the last five to ten y