@inproceedings{eisenberg-finlayson-2017-simpler,
title = "A Simpler and More Generalizable Story Detector using Verb and Character Features",
author = "Eisenberg, Joshua and
Finlayson, Mark",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1287",
doi = "10.18653/v1/D17-1287",
pages = "2708--2715",
abstract = "Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14{\%} improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000{\%}) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features{--}ones related to events, and ones related to characters{--}totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance.",
}
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%0 Conference Proceedings
%T A Simpler and More Generalizable Story Detector using Verb and Character Features
%A Eisenberg, Joshua
%A Finlayson, Mark
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F eisenberg-finlayson-2017-simpler
%X Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14% improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000%) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features–ones related to events, and ones related to characters–totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance.
%R 10.18653/v1/D17-1287
%U https://aclanthology.org/D17-1287
%U https://doi.org/10.18653/v1/D17-1287
%P 2708-2715
Markdown (Informal)
[A Simpler and More Generalizable Story Detector using Verb and Character Features](https://aclanthology.org/D17-1287) (Eisenberg & Finlayson, EMNLP 2017)
ACL