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Main Authors: Rogers, Anna, Karpinska, Marzena, Gupta, Ankita, Lialin, Vladislav, Smelkov, Gregory, Rumshisky, Anna
Format: Preprint
Published: 2019
Subjects:
Online Access:https://arxiv.org/abs/1908.11443
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author Rogers, Anna
Karpinska, Marzena
Gupta, Ankita
Lialin, Vladislav
Smelkov, Gregory
Rumshisky, Anna
author_facet Rogers, Anna
Karpinska, Marzena
Gupta, Ankita
Lialin, Vladislav
Smelkov, Gregory
Rumshisky, Anna
contents For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation framework that achieves full coverage of all possible TLinks. To compare with the previous SOTA in dense temporal annotation, we perform full re-annotation of TimeBankDense corpus, which shows comparable agreement with a significant increase in density. We contribute TimeBankNT corpus (with each text fully annotated by two expert annotators), extensive annotation guidelines, open-source tools for annotation and conversion to TimeML format, baseline results, as well as quantitative and qualitative analysis of inter-annotator agreement.
format Preprint
id arxiv_https___arxiv_org_abs_1908_11443
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle NarrativeTime: Dense Temporal Annotation on a Timeline
Rogers, Anna
Karpinska, Marzena
Gupta, Ankita
Lialin, Vladislav
Smelkov, Gregory
Rumshisky, Anna
Computation and Language
For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation framework that achieves full coverage of all possible TLinks. To compare with the previous SOTA in dense temporal annotation, we perform full re-annotation of TimeBankDense corpus, which shows comparable agreement with a significant increase in density. We contribute TimeBankNT corpus (with each text fully annotated by two expert annotators), extensive annotation guidelines, open-source tools for annotation and conversion to TimeML format, baseline results, as well as quantitative and qualitative analysis of inter-annotator agreement.
title NarrativeTime: Dense Temporal Annotation on a Timeline
topic Computation and Language
url https://arxiv.org/abs/1908.11443