Frances Adriana Laureano De Leon


2021

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UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation
Frances Adriana Laureano De Leon | Harish Tayyar Madabushi | Mark Lee
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper describes the participation of the UoB-NLP team in the ProfNER-ST shared subtask 7a. The task was aimed at detecting the mention of professions in social media text. Our team experimented with two methods of improving the performance of pre-trained models: Specifically, we experimented with data augmentation through translation and the merging of multiple language inputs to meet the objective of the task. While the best performing model on the test data consisted of mBERT fine-tuned on augmented data using back-translation, the improvement is minor possibly because multi-lingual pre-trained models such as mBERT already have access to the kind of information provided through back-translation and bilingual data.

2020

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CS-Embed at SemEval-2020 Task 9: The Effectiveness of Code-switched Word Embeddings for Sentiment Analysis
Frances Adriana Laureano De Leon | Florimond Guéniat | Harish Tayyar Madabushi
Proceedings of the Fourteenth Workshop on Semantic Evaluation

The growing popularity and applications of sentiment analysis of social media posts has naturally led to sentiment analysis of posts written in multiple languages, a practice known as code-switching. While recent research into code-switched posts has focused on the use of multilingual word embeddings, these embeddings were not trained on code-switched data. In this work, we present word-embeddings trained on code-switched tweets, specifically those that make use of Spanish and English, known as Spanglish. We explore the embedding space to discover how they capture the meanings of words in both languages. We test the effectiveness of these embeddings by participating in SemEval 2020 Task 9: Sentiment Analysis on Code-Mixed Social Media Text. We utilised them to train a sentiment classifier that achieves an F-1 score of 0.722. This is higher than the baseline for the competition of 0.656, with our team (codalab username francesita) ranking 14 out of 29 participating teams, beating the baseline.