Collective segmentation and labeling of distant entities in information extraction (2004)
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@MISC{Sutton04collectivesegmentation,
author = {Charles Sutton},
title = {Collective segmentation and labeling of distant entities in information extraction},
year = {2004}
}
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Abstract
In information extraction, we often wish to identify all mentions of an entity, such as a person or organization. Traditionally, a group of words is labeled as an entity based only on local information. But information from throughout a document can be useful; for example, if the same word is used multiple times, it is likely to have the same label each time. We present a CRF that explicitly represents dependencies between the labels of pairs of similar words in a document. On a standard information extraction data set, we show that learning these dependencies leads to a 13.7% reduction in error on the field that had caused the most repetition errors. 1
