DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction

2018-06-01 · SEMEVAL 2018 6 · Youngmin Kim, Hyunju Lee

This paper describes a system attended in the SemEval-2018 Task 1 {``}Affect in tweets{''} that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with sentiment classification data as a source data (SemEval 2017 Task 4a). A transfer model structure consists of a source domain and a target domain. Additionally, we try a new dropout that is applied to LSTMs in the Group LSTM. Our system ranked 8th at the subtask 1a (emotion intensity regression). We also show various results with different architectures in the source, target and transfer models.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

General Classification regression Sentiment AnalysisSentiment ClassificationTransfer LearningWord Embeddings

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification

2020-11-18 · SEMEVAL 2020 · Daniel Palomino, Jose Ochoa-Luna

We present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline. …

Language Modeling Language Modelling Sentiment Analysis Sentiment Classification+1

Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages

2023-04-13 · Israel Abebe Azime, Sana Sabah Al-Azzawi, Atnafu Lambebo Tonja, Iyanuoluwa Shode 외

AfriSenti-SemEval Shared Task 12 of SemEval-2023. The task aims to perform monolingual sentiment classification (sub-task A) for 12 African languages, multilingual sentiment classification (sub-task B), and zero-shot sen…

Classification Sentiment Analysis Sentiment Classification Zero-shot Sentiment Classification

kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification

2020-09-08 · SEMEVAL 2020 · Jiaxiang Liu, Xuyi Chen, Shikun Feng, Shuohuan Wang 외

Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language. With the increasing communication between groups with different languages, this phenomen…

Classification General Classification Language Modeling Language Modelling+3

Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning

2019-08-01 · WS 2019 8 · Hassan Alhuzali, Sophia Ananiadou

The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs). ADR classification helps to identify negative effects of drugs, which can guide health profess…

General Classification Sentiment Analysis Transfer Learning

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

2023-04-13 · Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Seid Muhie Yimam, David Ifeoluwa Adelani 외

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEv…

Classification Sentiment Analysis Sentiment Classification zero-shot-classification+1