Jason Angel


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TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection
Jason Angel | Segun Aroyehun | Alexander Gelbukh
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022. Specifically we take part in Subtask A for the English language. The task aims to determine whether a text from social media (a tweet) is sarcastic or not. We model the problem using knowledge sources, a pre-trained language model on sentiment/emotion data and a dataset focused on intended sarcasm. Our submission ranked third place among 43 teams. In addition, we show a brief error analysis of our best model to investigate challenging examples for detecting sarcasm.


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NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier
Jason Angel | Segun Taofeek Aroyehun | Antonio Tamayo | Alexander Gelbukh
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Code-switching is a phenomenon in which two or more languages are used in the same message. Nowadays, it is quite common to find messages with languages mixed in social media. This phenomenon presents a challenge for sentiment analysis. In this paper, we use a standard convolutional neural network model to predict the sentiment of tweets in a blend of Spanish and English languages. Our simple approach achieved a F1-score of 0:71 on test set on the competition. We analyze our best model capabilities and perform error analysis to expose important difficulties for classifying sentiment in a code-switching setting.


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Complex Word Identification: Convolutional Neural Network vs. Feature Engineering
Segun Taofeek Aroyehun | Jason Angel | Daniel Alejandro Pérez Alvarez | Alexander Gelbukh
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other languages from the perspective of non-native speakers. Our goal is to compare two approaches: feature engineering and a deep neural network. Both approaches achieved comparable performance on the English test set. We demonstrated the flexibility of the deep-learning approach by using the same deep neural network setup in the Spanish track. Our systems achieved competitive results: all our systems were within 0.01 of the system with the best macro-F1 score on the test sets except on Wikipedia test set, on which our best system is 0.04 below the best macro-F1 score.