Title

Learning to Understand Web Site Update Requests

Date of Original Version

2005

Type

Conference Proceeding

Abstract or Table of Contents

Although Natural Language Processing (NLP) for

requests

for information has been well-studied, there has been little prior work on understanding requests to update information. In this paper, we propose an intelligent system that can process natural language website update requests semi-automatically. In particular, this system can analyze requests, posted via email, to update the factual content of individual tuples in a databasebacked website. Users’ messages are processed using a scheme decomposing their requests into a sequence of entity recognition and text classification tasks. Using a corpus generated by human-subject experiments, we experimentally evaluate the performance of this system, as well as its robustness in handling request types not seen in training, o ruser-specific language styles not seen in training.