News / Highlights

• [Funded summer opportunity] The 2022 SCALE summer research workshop will be on authorship analysis. If you are a researcher or student interested in modeling writing style, deep metric learning, uncertainty estimation, graph embeddings, or explainability for text models, please consider applying.
• Paper on universal author embeddings with Aleem, Marcus, and colleages at Lawrence Livermore National Labs accepted to EMNLP 2021!
• Paper with Aleem, Noah, Elizabeth, and Marcus accepted to NAACL 2021!
• Paper with Steven and David accepted to EMNLP 2020!
• Led the SCALE 2019 workshop on NER. With over 50 participants, this was the largest SCALE to date. Thanks everyone, especially the students!

Publications

• Learning Universal Authorship Representations. EMNLP (2021)
  Rafael Rivera-Soto, Olivia Miano, Juanita Ordonez, Barry Chen, Aleem Khan, Marcus Bishop and Nicholas Andrews
  [pdf]
• A Deep Metric Learning Approach to Account Linking. NAACL (2021)
  Aleem Khan, Elizabeth Fleming, Noah Schofield, Marcus Bishop, Nicholas Andrews
  [aclweb] [arxiv] [code + data]
• Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast - Choose Three. EMNLP (2020)
  Steven Reich, David Mueller, Nicholas Andrews
  [aclweb] [arxiv] [code]
• Sources of Transfer in Multilingual Named Entity Recognition. ACL (2020)
  David Mueller, Nicholas Andrews, Mark Dredze
  [aclweb] [paper] [bib] [arxiv] [video]
• Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning. RepL4NLP (2020)
  Mitchell A Gordon, Kevin Duh, Nicholas Andrews
  [aclweb] [paper] [bib] [arxiv] [video]
• Learning Invariant Representations of Social Media Users. EMNLP (2019)
  Nicholas Andrews and Marcus Bishop
  [aclweb] [arxiv] [code] [data] [video]
• Convolutions Are All You Need (For Classifying Character Sequences). EMNLP WNUT (2018)
  Zach Wood-Doughty, Nicholas Andrews, and Mark Dredze
  [pdf]
• Predicting Twitter User Demographics from Names Alone. NAACL PEOPLES (2018)
  Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, and Mark Dredze
  [pdf]
• Bayesian Modeling of Lexical Resources for Low-Resource Settings. ACL (2017)
  Nicholas Andrews, Mark Dredze, Benjamin Van Durme, and Jason Eisner
  [pdf] [code] [slides]
• Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation.
  SocialNLP (2016)
  Mark Dredze, Nicholas Andrews and Jay DeYoung
  [pdf]
• Generative Non-Markov Models for Information Extraction. Dissertation (2015)
  Nicholas Andrews (advised by Jason Eisner and Mark Dredze)
• Robust Entity Clustering via Phylogenetic Inference. ACL (2014)
  Nicholas Andrews, Jason Eisner, and Mark Dredze
  [pdf] [full paper] [bib] [code] [slides]
• PARMA: A Predicate Argument Aligner. ACL (2013)
  Travis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu, and Xuchen Yao
  [pdf] [bib]
• Name Phylogeny: A Generative Model of String Variation. EMNLP (2012)
  Nicholas Andrews, Jason Eisner, and Mark Dredze
  [pdf] [bib]
• Entity Clustering Across Languages. NAACL (2012)
  Spence Green, Nicholas Andrews, Matthew R. Gormley, Mark Dredze,
  and Christopher D. Manning
  [pdf] [bib]
• Transformation Process Priors. NeuIPS NP Bayes (2011)
  Nicholas Andrews and Jason Eisner
  [pdf] [bib]
• Cross-lingual Coreference Resolution. TR (2011)
  Spence Green, Nicholas Andrews, Matthew R. Gormley, Mark Dredze,
  and Christopher D. Manning
  [pdf]
• Seeded Discovery of Base Relations in Large Corpora. EMNLP (2008)
  Nicholas Andrews and Naren Ramakrishnan
  [pdf] [bib]
• Recent Developments in Document Clustering. TR (2008)
  Nicholas Andrews and Edward A. Fox
  [pdf]

Reviewing

• NeurIPS (2015, 2017, 2018, 2019, 2021)
• ICML (2015, 2016, 2017, 2018, 2020, 2021)
• EMNLP (2015, 2016, 2017, 2019, 2020, 2021)
• AISTATS (2017, 2018, 2019, 2020)
• AAAI (2018, 2020, 2021)
• NAACL (2013, 2016, 2021)
• ACL (2017, 2018, 2020, 2021)
• EACL (2017)
• ICLR (2021)