@inproceedings{kravchenko-pivovarova-2018-dl,
title = "{DL} Team at {S}em{E}val-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings",
author = "Kravchenko, Dmitry and
Pivovarova, Lidia",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1025",
doi = "10.18653/v1/S18-1025",
pages = "172--176",
abstract = "The paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence. We demonstrate that general-purpose word embeddings produces more informative sentence representation than lexicon features. However, combining lexicon features with embeddings yields higher performance than embeddings alone.",
}
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%0 Conference Proceedings
%T DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings
%A Kravchenko, Dmitry
%A Pivovarova, Lidia
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kravchenko-pivovarova-2018-dl
%X The paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence. We demonstrate that general-purpose word embeddings produces more informative sentence representation than lexicon features. However, combining lexicon features with embeddings yields higher performance than embeddings alone.
%R 10.18653/v1/S18-1025
%U https://aclanthology.org/S18-1025
%U https://doi.org/10.18653/v1/S18-1025
%P 172-176
Markdown (Informal)
[DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings](https://aclanthology.org/S18-1025) (Kravchenko & Pivovarova, SemEval 2018)
ACL