  Recognizing shallow linguistic patterns, such as basic syntactic
relationships between words, is a common task in applied natural language and
text processing. The common practice for approaching this task is by tedious
manual definition of possible pattern structures, often in the form of regular
expressions or finite automata. This paper presents a novel memory-based
learning method that recognizes shallow patterns in new text based on a
bracketed training corpus. The training data are stored as-is, in efficient
suffix-tree data structures. Generalization is performed on-line at recognition
time by comparing subsequences of the new text to positive and negative
evidence in the corpus. This way, no information in the training is lost, as
can happen in other learning systems that construct a single generalized model
at the time of training. The paper presents experimental results for
recognizing noun phrase, subject-verb and verb-object patterns in English.
Since the learning approach enables easy porting to new domains, we plan to
apply it to syntactic patterns in other languages and to sub-language patterns
for information extraction.
