Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis

2020-07-26 · SEMEVAL 2020 · Vinay Gopalan, Mark Hopkins

We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations. During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed $4^{th}$ out of 62 entries in the official system rankings.

📄 PDF Abstract BibTeX arXiv:2007.13061

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Sentiment Analysis Transfer Learning

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YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification Objective

2021-08-01 · SEMEVAL 2021 · Mayukh Sharma, Ilanthenral Kandasamy, W.b. Vasantha

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2018-06-01 · SEMEVAL 2018 6 · Naveen J R, Barathi Ganesh H. B., An Kumar M, 외

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EmbLexChange at SemEval-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes

2020-12-01 · SEMEVAL 2020 · Ehsaneddin Asgari, Christoph Ringlstetter, Hinrich Sch{\"u}tze

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RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection

2020-12-01 · SEMEVAL 2020 · Ran Iwamoto, Masahiro Yukawa

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