Fetching the paper…
Reading the bibliography…
The advent of contextual word embeddings -- representations of words which incorporate semantic and syntactic information from their context -- has led to tremendous improvements on a wide variety of NLP tasks.
The proof and measurement of association between two things
Charles Spearman. 1904 · 1904
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
A synopsis of linguistic theory, 1930-1955
J. R. Firth. 1957 · 1955
Earlier work this paper cites.
Context-independent and context-dependent information in concepts
L. Barsalou. 1982 · 1982
Earlier work this paper cites.
Overview of the TREC 2001 question answering track
Ellen M Voorhees. 2002 · 2001
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005 · 2005
Earlier work this paper cites.
Concept narrowing: The role of context-independent information
Paula Rubio-Fernández. 2008 · 2008
Earlier work this paper cites.
A study on similarity and relatedness using distributional and wordnet-based approaches
Eneko Agirre, Enrique Alfonseca, Keith B. Hall, Jana Kravalova, Marius Pasca, and Aitor Soroa. 2009 · 2009
Earlier work this paper cites.
Comparison of semantic similarity for different languages using the google n-gram corpus and second-order co-occurrence measures
Colette Joubarne and Diana Inkpen. 2011 · 2011
Earlier work this paper cites.
Better word representations with recursive neural networks for morphology
Thang Luong, Richard Socher, and Christopher D. Manning. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, Alex Perelygin, J. Wu, Jason Chuang, Christopher D. Manning, A. Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Simlex-999: Evaluating semantic models with (genuine) similarity estimation
Felix Hill, Roi Reichart, and Anna Korhonen. 2014 · 2014
Earlier work this paper cites.
The Stanford CoreNLP Natural Language Processing Toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Improving distributional similarity with lessons learned from word embeddings
Omer Levy, Y. Goldberg, and I. Dagan. 2015 · 2015
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Cited alongside, same era.
Problems with evaluation of word embeddings using word similarity tasks
Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, and Chris Dyer. 2016 · 2016
Cited alongside, same era.
Simverb-3500: A large-scale evaluation set of verb similarity
Daniela Gerz, Ivan Vulić, Felix Hill, Roi Reichart, and Anna Korhonen. 2016 · 2016
Cited alongside, same era.
Learning joint multilingual sentence representations with neural machine translation
Black is to criminal as caucasian is to police: Towards detecting, evaluating and removing multiclass bias in word embeddings
Thomas Manzini, Lim Yao Chong, Alan W. Black, and Yulia Tsvetkov. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Later among the works it cites.
Room to Glo: A systematic comparison of semantic change detection approaches with word embeddings
Philippa Shoemark, Farhana Ferdousi Liza, Dong Nguyen, Scott Hale, and Barbara McGillivray. 2019 · 2019
Later among the works it cites.
Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Holger Schwenk and Matthijs Douze. 2017 · 2017
Cited alongside, same era.
Ngram2vec: Learning improved word representations from ngram co-occurrence statistics
Zhe Zhao, Tao Liu, Shen Li, Bofang Li, and Xiaoyong Du. 2017 · 2017
Cited alongside, same era.
Word translation without parallel data
Guillaume Lample, Alexis Conneau, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018 · 2018
Cited alongside, same era.
Unsupervised learning of sentence embeddings using compositional n-gram features
Matteo Pagliardini, Prakhar Gupta, and Martin Jaggi. 2018 · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Biowordvec, improving biomedical word embeddings with subword information and mesh
Yijia Zhang, Qingyu Chen, Z. Yang, H. Lin, and Zhiyong Lu. 2019 · 2019
Later among the works it cites.
Combining BERT with Static Word Embeddings for Categorizing Social Media
Israa Alghanmi, Luis Espinosa Anke, and Steven Schockaert. 2020 · 2020
Later among the works it cites.
Interpreting pretrained contextualized representations via reductions to static embeddings
Rishi Bommasani, Kelly Davis, and Claire Cardie. 2020 · 2020
Later among the works it cites.
ELECTRA: pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
Later among the works it cites.
On the use of jargon and word embeddings to explore subculture within the reddit’s manosphere
T. Farrell, Óscar Araque, Miriam Fernández, and H. Alani. 2020 · 2020
Later among the works it cites.
Studying political bias via word embeddings
Joshua Gordon, Marzieh Babaeianjelodar, and Jeanna Matthews. 2020 · 2020
Later among the works it cites.
Content analysis of textbooks via natural language processing: Findings on gender, race, and ethnicity in texas u.s. history textbooks
Li Lucy, Dorottya Demszky, Patricia Bromley, and Dan Jurafsky. 2020 · 2020
Later among the works it cites.
Application of machine learning and word embeddings in the classification of cancer diagnosis using patient anamnesis
Andrés Alejandro Ramos Magna, Héctor Allende-Cid, Carla Taramasco, C. Becerra, and R. Figueroa. 2020 · 2020
Later among the works it cites.
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model
Timothee Mickus, Denis Paperno, Mathieu Constant, and Kees van Deemter. 2020 · 2020
Later among the works it cites.
Word embeddings for the analysis of ideological placement in parliamentary corpora
L. Rheault and C. Cochrane. 2020 · 2020
Later among the works it cites.
A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
Later among the works it cites.
Are all good word vector spaces isomorphic?
Ivan Vulic, Sebastian Ruder, and Anders Søgaard. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Later among the works it cites.