Record Details


Field	Value

Title	Automatic segmentation of text into structured records

Names	BORKAR, V DESHMUKH, K SARAWAGI, S
Date Issued	2001 (iso8601)
Abstract	In this paper we present a method for automatically segmenting unformatted text records into structured elements. Several useful data sources today are human-generated as continuous text whereas convenient usage requires the data to be organized as structured records. A prime motivation is the warehouse address cleaning problem of transforming dirty addresses stored in large corporate databases as a single text field into subfields like "City" and "Street". Existing tools rely on hand-tuned, domain-specific rule-based systems. We describe a tool DATAMOLD that learns to automatically extract structure when seeded with a small number of training examples. The tool enhances on Hidden Markov Models (HMM) to build a powerful probabilistic model that corroborates multiple sources of information including, the sequence of elements, their length distribution, distinguishing words from the vocabulary and an optional external data dictionary.; Experiments on real-life datasets yielded accuracy of 90% on Asian addresses and 99% on US addresses. In contrast, existing information extraction methods based on rule-learning techniques yielded considerably lower accuracy.
Genre	Article; Proceedings Paper
Identifier	SIGMOD RECORD,30(2)175-186

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