@inproceedings{cheng-etal-2017-factored,
title = "A Factored Neural Network Model for Characterizing Online Discussions in Vector Space",
author = "Cheng, Hao and
Fang, Hao and
Ostendorf, Mari",
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/D17-1243",
doi = "10.18653/v1/D17-1243",
pages = "2296--2306",
abstract = "We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.",
}
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%0 Conference Proceedings
%T A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
%A Cheng, Hao
%A Fang, Hao
%A Ostendorf, Mari
%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 cheng-etal-2017-factored
%X We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.
%R 10.18653/v1/D17-1243
%U https://aclanthology.org/D17-1243
%U https://doi.org/10.18653/v1/D17-1243
%P 2296-2306
Markdown (Informal)
[A Factored Neural Network Model for Characterizing Online Discussions in Vector Space](https://aclanthology.org/D17-1243) (Cheng et al., EMNLP 2017)
ACL