Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict a tweet{'}s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSemantic Textual SimilaritySentiment AnalysisTask 2text similarityWord EmbeddingsSimilar Papers 제목 키워드 기반
EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words
This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurren…
Task 2T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction
This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural …
Document ClassificationGeneral ClassificationHyperparameter OptimizationLanguage Identification+4Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine
We present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, emotion-, and color-related…
Task 2Word EmbeddingsUMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices?
In this paper, we present our system for assigning an emoji to a tweet based on the text. Each tweet was originally posted with an emoji which the task providers removed. Our task was to decide out of 20 emojis, which or…
Task 2NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict the most likely associated emoji in Eng…
Task 2Word Embeddings