  This work has been motivated by two long term goals: to understand how humans
learn language and to build programs that can understand language. Using a
representation that makes the relevant features explicit is a prerequisite for
successful learning and understanding. Therefore, I chose to represent
relations between individual words explicitly in my model. Lexical attraction
is defined as the likelihood of such relations. I introduce a new class of
probabilistic language models named lexical attraction models which can
represent long distance relations between words and I formalize this new class
of models using information theory.
  Within the framework of lexical attraction, I developed an unsupervised
language acquisition program that learns to identify linguistic relations in a
given sentence. The only explicitly represented linguistic knowledge in the
program is lexical attraction. There is no initial grammar or lexicon built in
and the only input is raw text. Learning and processing are interdigitated. The
processor uses the regularities detected by the learner to impose structure on
the input. This structure enables the learner to detect higher level
regularities. Using this bootstrapping procedure, the program was trained on
100 million words of Associated Press material and was able to achieve 60%
precision and 50% recall in finding relations between content-words. Using
knowledge of lexical attraction, the program can identify the correct relations
in syntactically ambiguous sentences such as ``I saw the Statue of Liberty
flying over New York.''
