Search Results for author: Nicholas Andrews

Found 15 papers, 6 papers with code

Learning Universal Authorship Representations

no code implementations EMNLP 2021 Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews

Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods.

Authorship Verification

A Deep Metric Learning Approach to Account Linking

2 code implementations NAACL 2021 Aleem Khan, Elizabeth Fleming, Noah Schofield, Marcus Bishop, Nicholas Andrews

We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and metadata of their corresponding document streams.

Metric Learning

Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three

no code implementations EMNLP 2020 Steven Reich, David Mueller, Nicholas Andrews

However, extending these methods to structured prediction is not always straightforward or effective; furthermore, a held-out calibration set may not always be available.

Machine Translation Named Entity Recognition +2

Sources of Transfer in Multilingual Named Entity Recognition

1 code implementation ACL 2020 David Mueller, Nicholas Andrews, Mark Dredze

However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data.

Fine-tuning Multilingual Named Entity Recognition +1

Learning Invariant Representations of Social Media Users

1 code implementation IJCNLP 2019 Nicholas Andrews, Marcus Bishop

The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking.

Metric Learning

Convolutions Are All You Need (For Classifying Character Sequences)

no code implementations WS 2018 Zach Wood-Doughty, Nicholas Andrews, Mark Dredze

While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences.

Classification Document Classification +3

Predicting Twitter User Demographics from Names Alone

1 code implementation WS 2018 Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze

Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.

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