ConSSED

Repository for Configurable Semantic and Sentiment Emotion Detector (ConSSED) - our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text (https://www.humanizing-ai.com/emocontext.html).

Publication:

ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector

Citation:

@InProceedings{ConSSED-2019,
  author    = {Poświata, Rafał},
  title     = {ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019)},
  year = {2019},
  pages = {175–179}
}

Installation steps

1. Download the repository

git clone https://github.com/rafalposwiata/conssed.git

2. Create docker image

docker build -t conssed .

3. Download resources

Resources necessary for reconstructing the results from the publication are available here.

4. Complete resources

Due to the fact that some of the resources we used are protected by certain restrictions, we could not add them to the resources folder. In order to use the ConSSED system, two types of missing resources must be filled in: data and embeddings. Data shared by EmoContext organizers (train.txt, dev.txt and test.txt files) should be added to the data folder. Embeddings should be added according to the table below.

Embedding	File	Source link	Destination directory
GloVe	glove.twitter.27B.100d.txt	http://nlp.stanford.edu/data/glove.twitter.27B.zip	resources/embeddings/glove
NTUA_310	ntua_twitter_affect_310.txt	https://drive.google.com/open?id=1b-w7xf0d4zFmVoe9kipBHUwfoefFvU2t	resources/embeddings/word2vec
SSWE	sswe-r.txt	http://ir.hit.edu.cn/~dytang/paper/sswe/embedding-results.zip	resources/embeddings/sswe
Emo2Vec	emo2vec.txt	https://drive.google.com/file/d/1K0RPGSlBHOng4NN4Jkju_OkYtrmqimLi/view?usp=sharing	resources/embeddings/emo2vec

Reconstruction of the results from the publication

docker run --runtime=nvidia -v /path-to-resources-directory/:/resources conssed python3.6 /conssed/predict.py /resources/models/<model_name>/predict.config

Where <model_name> is one of the trained models, the list of which is as follows:

• BiLSTM_GloVe
• BiLSTM_ELMo
• BiLSTM_NTUA_310
• BiLSTM_SSWE
• BiLSTM_Emo2Vec
• ConSSED_GloVe_SSWE
• ConSSED_GloVe_Emo2Vec
• ConSSED_ELMo_SSWE
• ConSSED_ELMo_Emo2Vec
• ConSSED_NTUA_310_SSWE
• ConSSED_NTUA_310_Emo2Vec
• ConSSED_NTUA_310_Emo2Vec_v2