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Annotation projection for temporal information extraction

Published online by Cambridge University Press:  15 May 2019

Chris R. Giannella*
Affiliation:
The MITRE Corporation, 7515 Colshire Dr., McLean, VA 22102, USA
Ransom K. Winder
Affiliation:
The MITRE Corporation, 7515 Colshire Dr., McLean, VA 22102, USA
Joseph P. Jubinski
Affiliation:
The MITRE Corporation, 7515 Colshire Dr., McLean, VA 22102, USA
*
*Corresponding author. Email: cgiannella@mitre.org

Abstract

Approaches to building temporal information extraction systems typically rely on large, manually annotated corpora. Thus, porting these systems to new languages requires acquiring large corpora of manually annotated documents in the new languages. Acquiring such corpora is difficult owing to the complexity of temporal information extraction annotation. One strategy for addressing this difficulty is to reduce or eliminate the need for manually annotated corpora through annotation projection. This technique utilizes a temporal information extraction system for a source language (typically English) to automatically annotate the source language side of a parallel corpus. It then uses automatically generated word alignments to project the annotations, thereby creating noisily annotated target language training data. We developed an annotation projection technique for producing target language temporal information extraction systems. We carried out an English (source) to French (target) case study wherein we compared a French temporal information extraction system built using annotation projection with one built using a manually annotated French corpus. While annotation projection has been applied to building other kinds of Natural Language Processing tools (e.g., Named Entity Recognizers), to our knowledge, this is the first paper examining annotation projection as applied to temporal information extraction where no manual corrections of the target language annotations were made. We found that, even using manually annotated data to build a temporal information extraction system, F-scores were relatively low (<0.35), which suggests that the problem is challenging even with manually annotated data. Our annotation projection approach performed well (relative to the system built from manually annotated data) on some aspects of temporal information extraction (e.g., event–document creation time temporal relation prediction), but it performed poorly on the other kinds of temporal relation prediction (e.g., event–event and event–time).

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Article
Copyright
© Cambridge University Press 2019 

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