Rebecca Crowley and Kevin Mitchell
Medical Reports are an important and fertile area for Natural Language
Processing. Information from these free-text documents would be extremely
valuable if it could be automatically extracted and combined with other
data. However, Information Extraction from medical text poses significant
challenges. We describe the early development of a system for Information
Extractipn from Surgical Pathology Reports - a document which contains
essential data related to Cancer diagnosis and prognosis. It includes a
GATE implementation of NegEx - Wendy Chapman's algorithm for negation
detection. We will spend the first half of the talk describing our system
and detailing an evaluation of the Negation tagger compared to a
human-annotated corpus of negations. In the second half of the talk -
we'll show you a set of human annotated examples of attribute:value pairs
and shamelessly solicit advice on how to best extract them.
Speaker: Yanna Shen
Abstract:
Question Answering has become a growing interest in the NLP area in recent
years. But Chinese Question Answering systems still lack behind, so I am
interested in doing some work in Chinese Question Answering systems.
This work was done with other fellow students in NLP Laboratory at
Northeastern University, China. We just borrowed some ideas from several
QA papers and built a small QA demo. Then we tried to utilize these ideas
into the Chinese QA system.
In this talk, I will discuss the design of the demo, and give a few points
in building a Chinese QA system.