A Maximum Entropy Approach to Information Extraction from Semi-Structured and Free Text

Hai Leong Chieu, DSO National Laboratories; Hwee Tou Ng, National University of Singapore

In this paper, we present a classification-based approach towards single-slot as well as multi-slot information extraction (IE). For single-slot IE, we worked on the domain of Seminar Announcements, where each document contains information on only one seminar. For multi-slot IE, we worked on the domain of Management Succession. For this domain, we restrict ourselves to extracting information sentence by sentence, in the same way as (Soderland 1999). Each sentence can contain information on several management succession events. By using a classification approach based on a maximum entropy framework, our system achieves higher accuracy than the best previously published results in both domains.

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