  This paper describes a natural language parsing algorithm for unrestricted
text which uses a probability-based scoring function to select the "best" parse
of a sentence. The parser, Pearl, is a time-asynchronous bottom-up chart parser
with Earley-type top-down prediction which pursues the highest-scoring theory
in the chart, where the score of a theory represents the extent to which the
context of the sentence predicts that interpretation. This parser differs from
previous attempts at stochastic parsers in that it uses a richer form of
conditional probabilities based on context to predict likelihood. Pearl also
provides a framework for incorporating the results of previous work in
part-of-speech assignment, unknown word models, and other probabilistic models
of linguistic features into one parsing tool, interleaving these techniques
instead of using the traditional pipeline architecture. In preliminary tests,
Pearl has been successful at resolving part-of-speech and word (in speech
processing) ambiguity, determining categories for unknown words, and selecting
correct parses first using a very loosely fitting covering grammar.
