@inproceedings{pivovarova-etal-2017-hcs,
title = "{HCS} at {S}em{E}val-2017 Task 5: Polarity detection in business news using convolutional neural networks",
author = "Pivovarova, Lidia and
Escoter, Lloren{\c{c}} and
Klami, Arto and
Yangarber, Roman",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2143",
doi = "10.18653/v1/S17-2143",
pages = "842--846",
abstract = "Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.",
}
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%0 Conference Proceedings
%T HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks
%A Pivovarova, Lidia
%A Escoter, Llorenç
%A Klami, Arto
%A Yangarber, Roman
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F pivovarova-etal-2017-hcs
%X Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.
%R 10.18653/v1/S17-2143
%U https://aclanthology.org/S17-2143
%U https://doi.org/10.18653/v1/S17-2143
%P 842-846
Markdown (Informal)
[HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks](https://aclanthology.org/S17-2143) (Pivovarova et al., SemEval 2017)
ACL