Fetching the paper…
Reading the bibliography…
Despite an ever growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models.
Linspector web: A multilingual probing suite for word representations
Eichler, Max, Gözde Gül Şahin, and Iryna Gurevych. 2019 · 1907
Earlier work this paper cites.
Contextual correlates of synonymy
Rubenstein, Herbert and John B. Goodenough. 1965 · 1965
Earlier work this paper cites.
Contextual correlates of semantic similarity
Miller, George A and Walter G Charles. 1991 · 1991
Earlier work this paper cites.
A new algorithm for data compression
Gage, Philip. 1994 · 1994
Earlier work this paper cites.
The great daghestanian case hoax
Comrie, Bernard and Maria Polinsky. 1998 · 1998
Earlier work this paper cites.
Case . Cambridge University Press
Blake, Barry J. 2001 · 2001
Earlier work this paper cites.
Placing search in context: the concept revisited
Finkelstein, Lev, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001 · 2001
Earlier work this paper cites.
Introduction to the conll-2002 shared task: Language-independent named entity recognition
Sang, Erik F. Tjong Kim. 2002 · 2002
Earlier work this paper cites.
Online large-margin training of dependency parsers
McDonald, Ryan, Koby Crammer, and Fernando Pereira. 2005 · 2005
Earlier work this paper cites.
Understanding the value of features for coreference resolution
Bengtson, Eric and Dan Roth. 2008 · 2008
Earlier work this paper cites.
Dependency parsing of turkish
Eryigit, Gülsen, Joakim Nivre, and Kemal Oflazer. 2008 · 2008
Earlier work this paper cites.
The conll-2009 shared task: Syntactic and semantic dependencies in multiple languages
Hajič, Jan, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antònia Martí, Lluís Màrquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Štěpánek, Pavel Straňák, Mihai Surdeanu, Nianwen Xue, and Yi Zhang. 2009 · 2009
Earlier work this paper cites.
Wuggy: A multilingual pseudoword generator
Keuleers, Emmanuel and Marc Brysbaert. 2010 · 2010
Earlier work this paper cites.
Distributional semantics in technicolor
Bruni, Elia, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012 · 2012
Earlier work this paper cites.
Getting more from morphology in multilingual dependency parsing
Hohensee, Matt and Emily M. Bender. 2012 · 2012
Earlier work this paper cites.
Improving word representations via global context and multiple word prototypes
Huang, Eric, Richard Socher, Christopher Manning, and Andrew Ng. 2012 · 2012
Earlier work this paper cites.
Number of genders
Corbett, Greville G. 2013 · 2013
Earlier work this paper cites.
Number of cases
Iggesen, Oliver A. 2013 · 2013
Earlier work this paper cites.
Better word representations with recursive neural networks for morphology
Luong, Minh-Thang, Richard Socher, and Christopher D. Manning. 2013 · 2013
Earlier work this paper cites.
Germeval 2014 named entity recognition shared task: Companion paper
Benikova, Darina, Chris Biemann, Max Kisselew, and Sebastian Padó · 2014
Earlier work this paper cites.
Turkish resources for visual word recognition
Erten, Begum, Cem Bozsahin, and Deniz Zeyrek. 2014 · 2014
Earlier work this paper cites.
Using morphosemantic information in construction of a pilot lexical semantic resource for turkish
Isgüder, Gözde Gül and Esref Adali. 2014 · 2014
Earlier work this paper cites.
Dependency-based word embeddings
Levy, Omer and Yoav Goldberg. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Bowman, Samuel R., Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Retrofitting word vectors to semantic lexicons
Faruqui, Manaal, Jesse Dodge, Sujay Kumar Jauhar, Chris Dyer, Eduard Hovy, and Noah A Smith. 2015 · 2015
Earlier work this paper cites.
The Finnish Proposition Bank
Haverinen, Katri, Jenna Kanerva, Samuel Kohonen, Anna Missila, Stina Ojala, Timo Viljanen, Veronika Laippala, and Filip Ginter. 2015 · 2015
Earlier work this paper cites.
Simlex-999: Evaluating semantic models with (genuine) similarity estimation
Hill, Felix, Roi Reichart, and Anna Korhonen. 2015 · 2015
Earlier work this paper cites.
Bidirectional lstm-crf models for sequence tagging
Huang, Zhiheng, Wei Xu, and Kai Yu. 2015 · 2015
Cited alongside, same era.
Separated by an un-common language: Towards judgment language informed vector space modeling
Leviant, Ira and Roi Reichart. 2015 · 2015
Cited alongside, same era.
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Ling, Wang, Chris Dyer, Alan W Black, Isabel Trancoso, Ramon Fermandez, Silvio Amir, Luis Marujo, and Tiago Luis. 2015 · 2015
Cited alongside, same era.
Evaluation methods for unsupervised word embeddings
Schnabel, Tobias, Igor Labutov, David M. Mimno, and Thorsten Joachims. 2015 · 2015
Cited alongside, same era.
A language-independent feature schema for inflectional morphology
Sylak-Glassman, John, Christo Kirov, David Yarowsky, and Roger Que. 2015 · 2015
Cited alongside, same era.
A survey of cross-lingual word embedding models
Ruder, Sebastian, Ivan Vulić, and Anders Søgaard. 2017 · 2017
Later among the works it cites.
Sahin, H. Bahadir, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren, and Omer Ozan Sonmez. 2017 · 2017
Later among the works it cites.
A simple regularization-based algorithm for learning cross-domain word embeddings
Yang, Wei, Wei Lu, and Vincent Zheng. 2017 · 2017
Later among the works it cites.
Compositional representation of morphologically-rich input for neural machine translation
Ataman, Duygu and Marcello Federico. 2018 · 2018
Later among the works it cites.
The lazy encoder: A fine-grained analysis of the role of morphology in neural machine translation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Common pitfalls in statistical analysis: The use of correlation techniques
Aggarwal, Rakesh and Priya Ranganathan. 2016 · 2016
Cited alongside, same era.
Deep biaffine attention for neural dependency parsing
Dozat, Timothy and Christopher D. Manning. 2016 · 2016
Cited alongside, same era.
Character-Aware Neural Language Models
Kim, Yoon, Yacine Jernite, David Sontag, and Alexander Rush. 2016 · 2016
Cited alongside, same era.
Evaluating embeddings using syntax-based classification tasks as a proxy for parser performance
Köhn, Arne. 2016 · 2016
Cited alongside, same era.
Issues in evaluating semantic spaces using word analogies
Linzen, Tal. 2016 · 2016
Cited alongside, same era.
Evaluating word embeddings using a representative suite of practical tasks
Nayak, Neha, Gabor Angeli, and Christopher D. Manning. 2016 · 2016
Cited alongside, same era.
Investigating language universal and specific properties in word embeddings
Qian, Peng, Xipeng Qiu, and Xuanjing Huang. 2016 · 2016
Cited alongside, same era.
Bisazza, Arianna and Clara Tump. 2018 · 2018
Later among the works it cites.
Towards better UD parsing: Deep contextualized word embeddings, ensemble, and treebank concatenation
Che, Wanxiang, Yijia Liu, Yuxuan Wang, Bo Zheng, and Ting Liu. 2018 · 2018
Later among the works it cites.
What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
Conneau, Alexis, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018a · 2018
Later among the works it cites.
Xnli: Evaluating cross-lingual sentence representations
Conneau, Alexis, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. 2018b · 2018
Later among the works it cites.
Are all languages equally hard to language-model?
Cotterell, Ryan, Sebastian J. Mielke, Jason Eisner, and Brian Roark. 2018 · 2018
Later among the works it cites.
Annotation of semantic roles for the turkish proposition bank
Şahin, Gözde Gül and Eşref Adalı. 2018 · 2018
Later among the works it cites.
Character-level models versus morphology in semantic role labeling
Şahin, Gözde Gül and Mark Steedman. 2018 · 2018
Later among the works it cites.
Language modeling for morphologically rich languages: Character-aware modeling for word-level prediction
Gerz, Daniela, Ivan Vulić, Edoardo Maria Ponti, Jason Naradowsky, Roi Reichart, and Anna Korhonen. 2018 · 2018
Later among the works it cites.
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
Heinzerling, Benjamin and Michael Strube. 2018 · 2018
Later among the works it cites.
UniMorph 2.0: Universal morphology
Kirov, Christo, Ryan Cotterell, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sebastian Mielke, Arya McCarthy, Sandra Kübler, David Yarowsky, Jason Eisner, and Mans Hulden. 2018 · 2018
Later among the works it cites.
Marrying universal dependencies and universal morphology
McCarthy, Arya D., Miikka Silfverberg, Ryan Cotterell, Mans Hulden, and David Yarowsky. 2018 · 2018
Later among the works it cites.
Universal dependencies 2.3
Nivre, Joakim et. al. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Peters, Matthew E., Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
What’s in your embedding, and how it predicts task performance
Rogers, Anna, Shashwath Hosur Ananthakrishna, and Anna Rumshisky. 2018 · 2018
Later among the works it cites.
Proceedings of the 2018 emnlp workshop blackboxnlp: Analyzing and interpreting neural networks for nlp
Tal Linzen, Tal, Grzegorz Chrupała, and Afra Alishahi. 2018 · 2018
Later among the works it cites.
What do character-level models learn about morphology? the case of dependency parsing
Vania, Clara, Andreas Grivas, and Adam Lopez. 2018 · 2018
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Williams, Adina, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Later among the works it cites.
Analysis methods in neural language processing: A survey
Belinkov, Yonatan and James Glass. 2019 · 2019
Closest in time.
On the complexity and typology of inflectional morphological systems
Cotterell, Ryan, Christo Kirov, Mans Hulden, and Jason Eisner. 2019 · 2019
Closest in time.
What do you learn from context? probing for sentence structure in contextualized word representations
Tenney, Ian, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Sam Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
Closest in time.
From characters to words to in between: Do we capture morphology?
Vania, Clara and Adam Lopez. 2017 · 2027
Closest in time.
Evaluation of word vector representations by subspace alignment
Tsvetkov, Yulia, Manaal Faruqui, Wang Ling, Guillaume Lample, and Chris Dyer. 2015 · 2054
Closest in time.
What’s in an embedding? analyzing word embeddings through multilingual evaluation
Köhn, Arne. 2015 · 2073
Closest in time.