Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption
This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media {---} more precisely, Twitter {---} with its aim to…
ClassificationGeneral ClassificationSentiment AnalysisTask 2+4PickleTeam! at SemEval-2018 Task 2: English and Spanish Emoji Prediction from Tweets
We present a system for emoji prediction on English and Spanish tweets, prepared for the SemEval-2018 task on Multilingual Emoji Prediction. We compared the performance of an SVM, LSTM and an ensemble of these two. We fo…
Medical DiagnosisPredictionTask 2YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction
This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish. We normalized text-based tweets during pre-processing, following whic…
Sentiment AnalysisTask 2DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets
This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team. First, we employed a combination of pre-processing techniques to reduce the noise of tweets a…
Information RetrievalTask 2TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction
Emojis are widely used on social media andunderstanding their meaning is important forboth practical purposes (e.g. opinion mining,sentiment detection) and theoretical purposes(e.g. how different L1 speakers use them, do…
Opinion MiningSentiment AnalysisTask 2Word EmbeddingsSyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons
In this paper, we present SyntNN as a way to include traditional syntactic models in multilayer neural networks used in the task of Semeval Task 2 of emoji prediction. The model builds on the distributed tree embedder al…
Task 2CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation
Emoji is one of the {``}fastest growing language {''} in pop-culture, especially in social media and it is very unlikely for its usage to decrease. These are generally used to bring an extra level of meaning to the texts…
Cultural Vocal Bursts Intensity PredictionOpinion MiningSentiment AnalysisTask 2Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets
We present our submission to the Semeval 2018 task on emoji prediction. We used a random forest, with an ensemble of bag-of-words, sentiment and psycholinguistic features. Although we performed well on the trial dataset …
Task 2Text ClassificationTweety 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 EmbeddingsLIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction
In this paper we present the system submitted to the SemEval2018 task2 : Multilingual Emoji Prediction. Our system approaches both languages as being equal by first; considering word embeddings associated to automaticall…
Information RetrievalTask 2Word EmbeddingsIronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media
This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, {``}Irony Detection in English Tweets{''}. In Task 1, irony classification task has been considered as a binary classi…
Binary ClassificationGeneral ClassificationTask 2Text ClassificationDigital Operatives at SemEval-2018 Task 8: Using dependency features for malware NLP
The four sub-tasks of SecureNLP build towards a capability for quickly highlighting critical information from malware reports, such as the specific actions taken by a malware sample. Digital Operatives (DO) submitted to …
Task 2text-classificationText ClassificationUMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding
This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural N…
Task 2Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling
This paper describes the Duluth UROP systems that participated in SemEval--2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of ensembles made up of classifiers using Naive Bayes, Logistic Regression, an…
Ensemble LearningregressionTask 2Overcoming catastrophic forgetting problem by weight consolidation and long-term memory
Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during learning of new, disjoint knowledge. Here…
Task 2NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake
Our team, NRC-Canada, participated in two shared tasks at the AMIA-2017 Workshop on Social Media Mining for Health Applications (SMM4H): Task 1 - classification of tweets mentioning adverse drug reactions, and Task 2 - c…
General ClassificationTask 2Word EmbeddingsCLaC @ DEFT 2018: Sentiment analysis of tweets on transport from \^Ile-de-France
CLaC @ DEFT 2018: Analysis of tweets on transport on the {\^I}le-de-France This paper describes the system deployed by the CLaC lab at Concordia University in Montreal for the DEFT 2018 shared task. The competition consi…
Sentiment AnalysisTask 2Adapted Sentiment Similarity Seed Words For French Tweets' Polarity Classification
We present, in this paper, our contribution in DEFT 2018 task 2 : {``}Global polarity{''}, determining the overall polarity (Positive, Negative, Neutral or MixPosNeg) of tweets regarding public transport, in French langu…
General ClassificationTask 2Word EmbeddingsForecasting the presence and intensity of hostility on Instagram using linguistic and social features
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. Wh…
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