  Information extraction is the task of automatically picking up information of
interest from an unconstrained text. Information of interest is usually
extracted in two steps. First, sentence level processing locates relevant
pieces of information scattered throughout the text; second, discourse
processing merges coreferential information to generate the output. In the
first step, pieces of information are locally identified without recognizing
any relationships among them. A key word search or simple pattern search can
achieve this purpose. The second step requires deeper knowledge in order to
understand relationships among separately identified pieces of information.
Previous information extraction systems focused on the first step, partly
because they were not required to link up each piece of information with other
pieces. To link the extracted pieces of information and map them onto a
structured output format, complex discourse processing is essential. This paper
reports on a Japanese information extraction system that merges information
using a pattern matcher and discourse processor. Evaluation results show a high
level of system performance which approaches human performance.
