Stan Matwin


2020

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SemEval-2020 Task 5: Counterfactual Recognition
Xiaoyu Yang | Stephen Obadinma | Huasha Zhao | Qiong Zhang | Stan Matwin | Xiaodan Zhu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequent with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. Our data and baseline code are made publicly available at https://zenodo.org/record/3932442. The task website and leaderboard can be found at https://competitions.codalab.org/competitions/21691.

2019

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Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities
Sima Sharifirad | Stan Matwin
Proceedings of the 2019 Workshop on Widening NLP

Social media posts reflect the emotions, intentions and mental state of the users. Twitter users who harass famous female figures may do so with different intentions and intensities. Recent studies have published datasets focusing on different types of online harassment, vulgar language, and emotional intensities. We trained, validate and test our proposed model, attention-based bidirectional neural network, on the three datasets:”online harassment”, “vulgar language” and “valance” and achieved state of the art performance in two of the datasets. We report F1 score for each dataset separately along with the final precision, recall and macro-averaged F1 score. In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter. We tested the trained models on ten collected corpuses each related to one famous female figure to predict the type of harassing language, the type of vulgar language and the degree of intensity of language occurring on their social platforms. Interestingly, the achieved results show different patterns of linguistic use targeting different racial background and occupations. The contribution of this study is two-fold. From the technical perspective, our proposed methodology is shown to be effective with a good margin in comparison to the previous state-of-the-art results on one of the two available datasets. From the social perspective, we introduce a methodology which can unlock facts about the nature of offensive language targeting women on online social platforms. The collected dataset will be shared publicly for further investigation.

2018

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DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning
Habibeh Naderi | Behrouz Haji Soleimani | Saif Mohammad | Svetlana Kiritchenko | Stan Matwin
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop a multi-aspect feature learning mechanism to capture the most discriminative semantic features of a tweet as well as the emotion information conveyed by each word in it. We combine six types of feature groups: (1) a tweet representation learned by an LSTM deep neural network on the training data, (2) a tweet representation learned by an LSTM network on a large corpus of tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams, (5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features. As part of the word embedding training, we also learn the distributed representations of multi-word expressions (MWEs) and negated forms of words. An SVR regressor is then trained over the full set of features. We evaluate the effectiveness of our ensemble feature sets on the SemEval-2018 Task 1 datasets and achieve a Pearson correlation of 72% on the task of tweet emotion intensity prediction.

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Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs
Sima Sharifirad | Borna Jafarpour | Stan Matwin
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An interesting solution to poor datasets is to take advantage of the world knowledge in the form of knowledge graphs to improve our training data. In this paper, we use ConceptNet and Wikidata to improve sexist tweet classification by two methods (1) text augmentation and (2) text generation. In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity. In our text augmentation approach, the number of tweets remains the same but their words are augmented (concatenation) with words extracted from their ConceptNet relations and their description extracted from Wikidata. In our text augmentation approach, the number of tweets in each class remains the same but the range of each tweet increases. Our experiments show that our approach improves sexist tweet classification significantly in our entire machine learning models. Our approach can be readily applied to any other small dataset size like hate speech or abusive language and text classification problem using any machine learning model.

2015

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From Argumentation Mining to Stance Classification
Parinaz Sobhani | Diana Inkpen | Stan Matwin
Proceedings of the 2nd Workshop on Argumentation Mining

2013

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Authorship Attribution in Health Forums
Victoria Bobicev | Marina Sokolova | Khaled El Emam | Stan Matwin
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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How Joe and Jane Tweet about Their Health: Mining for Personal Health Information on Twitter
Marina Sokolova | Stan Matwin | Yasser Jafer | David Schramm
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2010

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Building Systematic Reviews Using Automatic Text Classification Techniques
Oana Frunza | Diana Inkpen | Stan Matwin
Coling 2010: Posters

1998

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Experiments with Learning Parsing Heuristics
Sylvain Delisle | Sylvain Letourneau | Stan Matwin
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Experiments with Learning Parsing Heuristics
Sylvain Delisle | Sylvain Létourneau | Stan Matwin
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Expérimentation en apprentissage d'heuristiques pour l'analyse syntaxique
Sylvain Delisle | Sylvain Létourneau | Stan Matwin
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Text Classification Using WordNet Hypernyms
Sam Scott | Stan Matwin
Usage of WordNet in Natural Language Processing Systems