ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge

• Main Authors: Filannino, Michele, Brown, Gavin, Nenadic, Goran
• Format: Journal Article
• Language: English
• Published: 30-04-2013
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.1304.7942

This paper describes a temporal expression identification and normalization
system, ManTIME, developed for the TempEval-3 challenge. The identification
phase combines the use of conditional random fields along with a
post-processing identification pipeline, whereas the normalization phase is
carried out using NorMA, an open-source rule-based temporal normalizer. We
investigate the performance variation with respect to different feature types.
Specifically, we show that the use of WordNet-based features in the
identification task negatively affects the overall performance, and that there
is no statistically significant difference in using gazetteers, shallow parsing
and propositional noun phrases labels on top of the morphological features. On
the test data, the best run achieved 0.95 (P), 0.85 (R) and 0.90 (F1) in the
identification phase. Normalization accuracies are 0.84 (type attribute) and
0.77 (value attribute). Surprisingly, the use of the silver data (alone or in
addition to the gold annotated ones) does not improve the performance.