Derek Ruths


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Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage
Jana Kurrek | Haji Mohammad Saleem | Derek Ruths
Proceedings of the Fourth Workshop on Online Abuse and Harms

Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.


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An Attribution Relations Corpus for Political News
Edward Newell | Drew Margolin | Derek Ruths
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Sentiment Analysis: It’s Complicated!
Kian Kenyon-Dean | Eisha Ahmed | Scott Fujimoto | Jeremy Georges-Filteau | Christopher Glasz | Barleen Kaur | Auguste Lalande | Shruti Bhanderi | Robert Belfer | Nirmal Kanagasabai | Roman Sarrazingendron | Rohit Verma | Derek Ruths
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with such annotations, it is common for dataset constructors to discard “noisy” or “controversial” data where there is significant disagreement on the proper label. In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30% of the originally annotated data. We argue that the removal of such data is a problematic trend because, when performing real-time sentiment classification of short-text, an automated system cannot know a priori which samples would fall into this category of disputed sentiment. We therefore propose the notion of a “complicated” class of sentiment to categorize such text, and argue that its inclusion in the short-text sentiment analysis framework will improve the quality of automated sentiment analysis systems as they are implemented in real-world settings. We motivate this argument by building and analyzing a new publicly available TSA dataset of over 7,000 tweets annotated with 5x coverage, named MTSA. Our analysis of classifier performance over our dataset offers insights into sentiment analysis dataset and model design, how current techniques would perform in the real world, and how researchers should handle difficult data.

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A Hierarchical Neural Attention-based Text Classifier
Koustuv Sinha | Yue Dong | Jackie Chi Kit Cheung | Derek Ruths
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.


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Assessing the Verifiability of Attributions in News Text
Edward Newell | Ariane Schang | Drew Margolin | Derek Ruths
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

When reporting the news, journalists rely on the statements of stakeholders, experts, and officials. The attribution of such a statement is verifiable if its fidelity to the source can be confirmed or denied. In this paper, we develop a new NLP task: determining the verifiability of an attribution based on linguistic cues. We operationalize the notion of verifiability as a score between 0 and 1 using human judgments in a comparison-based approach. Using crowdsourcing, we create a dataset of verifiability-scored attributions, and demonstrate a model that achieves an RMSE of 0.057 and Spearman’s rank correlation of 0.95 to human-generated scores. We discuss the application of this technique to the analysis of mass media.

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Vectors for Counterspeech on Twitter
Lucas Wright | Derek Ruths | Kelly P Dillon | Haji Mohammad Saleem | Susan Benesch
Proceedings of the First Workshop on Abusive Language Online

A study of conversations on Twitter found that some arguments between strangers led to favorable change in discourse and even in attitudes. The authors propose that such exchanges can be usefully distinguished according to whether individuals or groups take part on each side, since the opportunity for a constructive exchange of views seems to vary accordingly.


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Annotating Characters in Literary Corpora: A Scheme, the CHARLES Tool, and an Annotated Novel
Hardik Vala | Stefan Dimitrov | David Jurgens | Andrew Piper | Derek Ruths
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Characters form the focus of various studies of literary works, including social network analysis, archetype induction, and plot comparison. The recent rise in the computational modelling of literary works has produced a proportional rise in the demand for character-annotated literary corpora. However, automatically identifying characters is an open problem and there is low availability of literary texts with manually labelled characters. To address the latter problem, this work presents three contributions: (1) a comprehensive scheme for manually resolving mentions to characters in texts. (2) A novel collaborative annotation tool, CHARLES (CHAracter Resolution Label-Entry System) for character annotation and similiar cross-document tagging tasks. (3) The character annotations resulting from a pilot study on the novel Pride and Prejudice, demonstrating the scheme and tool facilitate the efficient production of high-quality annotations. We expect this work to motivate the further production of annotated literary corpora to help meet the demand of the community.

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The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors
Hardik Vala | Andrew Piper | Derek Ruths
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On The Difficulty of Detecting Characters in Literary Texts
Hardik Vala | David Jurgens | Andrew Piper | Derek Ruths
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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Twitter Users #CodeSwitch Hashtags! #MoltoImportante #wow
David Jurgens | Stefan Dimitrov | Derek Ruths
Proceedings of the First Workshop on Computational Approaches to Code Switching


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Gender Inference of Twitter Users in Non-English Contexts
Morgane Ciot | Morgan Sonderegger | Derek Ruths
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing