Felipe Bravo-Marquez


2020

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DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings
Frank D. Zamora-Reina | Felipe Bravo-Marquez
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another. Our system employs the temporal referencing method to obtain compatible representations of target words in different periods of time. This is done by concatenating corpora of different periods and performing a temporal referencing of target words i.e., treating occurrences of target words in different periods as two independent tokens. Afterwards, we train word embeddings on the joint corpus and compare the referenced vectors of each target word using cosine similarity. Our submission was ranked 7th among 34 teams for subtask 1, obtaining an average accuracy of 0.637, only 0.050 points behind the first ranked system.

2019

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MāOri Loanwords: A Corpus of New Zealand English Tweets
David Trye | Andreea Calude | Felipe Bravo-Marquez | Te Taka Keegan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Māori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the Māori community. Motivated by the lack of linguistic resources for studying how Māori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected Māori words that are likely to be known by New Zealanders who do not speak Māori. Since over 30% of these words turned out to be irrelevant, we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets.

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An ELMo-inspired approach to SemDeep-5’s Word-in-Context task
Alan Ansell | Felipe Bravo-Marquez | Bernhard Pfahringer
Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)

2018

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SemEval-2018 Task 1: Affect in Tweets
Saif Mohammad | Felipe Bravo-Marquez | Mohammad Salameh | Svetlana Kiritchenko
Proceedings of The 12th International Workshop on Semantic Evaluation

We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.

2017

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Emotion Intensities in Tweets
Saif Mohammad | Felipe Bravo-Marquez
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best–worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language.

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WASSA-2017 Shared Task on Emotion Intensity
Saif Mohammad | Felipe Bravo-Marquez
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.