Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification
In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag-of-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations. In particular, we trained robust task-oriented subword-aware embeddings and computed tweet representations using a weighted-averaging strategy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSentiment AnalysisWord EmbeddingsSimilar Papers 제목 키워드 기반
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis
In this article we describe our participation in TASS 2019, a shared task aimed at the detection of sentiment polarity of Spanish tweets. We combined different representations such as bag-of-words, bag-of-characters, and…
Data AugmentationSentiment AnalysisTranslationWord EmbeddingsUWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets
This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English,…
ClassificationGeneral ClassificationOrdinal ClassificationPOS+2\#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets
In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression.…
General ClassificationInformation RetrievalregressionSentiment Analysis+2Using Author Embeddings to Improve Tweet Stance Classification
Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification {--} where a tweet is categorized according to a viewpo…
ClassificationGeneral ClassificationStance ClassificationUSF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings
In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In…
General ClassificationLemmatizationWord Embeddings