---
title: "Learning Gender-Neutral Word Embeddings — Zhao 2018"
description: "Word vectors trained on human-written text pick up gender stereotypes."
authors: ["Jieyu Zhao", "Yichao Zhou", "Zeyu Li", "Wei Wang", "Kai-Wei Chang"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1521/"
doi: "10.18653/v1/d18-1521"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2018](https://md.research.elamur.ai/free/en/papers/2018/) / Learning Gender-Neutral Word Embeddings

# Learning Gender-Neutral Word Embeddings

**Authors:** [Jieyu Zhao](https://md.research.elamur.ai/free/en/authors/jieyu-zhao/), Yichao Zhou, Zeyu Li, [Wei Wang](https://md.research.elamur.ai/free/en/authors/wei-wang/), [Kai-Wei Chang](https://md.research.elamur.ai/free/en/authors/kai-wei-chang/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1521/) · [PDF](https://aclanthology.org/D18-1521.pdf) · [DOI 10.18653/v1/d18-1521](https://doi.org/10.18653/v1/d18-1521) · [OpenAlex](https://openalex.org/W2889624842)

**Citations:** 345 citations (openalex-snapshot · 2026-10-14) · [167](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Word vectors trained on human-written text pick up gender stereotypes. Occupation words such as "programmer" or "doctor" drift toward one gender, and that bias can carry into systems like coreference resolvers or resume screeners. An earlier remedy by Bolukbasi et al. (2016), used here as the Hard-GloVe baseline, removes bias after training. It has two drawbacks: it relies on a separate classifier to decide which words should be neutral, and it erases gender information entirely. This paper instead handles bias during training. GN-GloVe extends the GloVe objective with two extra terms. The first concentrates the gender signal of male- and female-definition seed words (taken from WordNet) in a single reserved dimension. The second keeps the remaining dimensions of gender-neutral words orthogonal to a gender direction that is re-estimated at each epoch. The model is trained on a 2017 English Wikipedia dump. It almost always identifies true gender pairs in an analogy test and keeps word-similarity quality, losing only a little on analogies. It also narrows the stereotype gap in coreference. The method works for any language with a gender seed list, and the code is public.

## Key points
- **Method:** The embedding is split into a neutral part w(a) and a gendered part w(g), with k = 1. The method has two variants of the gender-separation loss (L1 and L2), and λd = λe = 0.8.
- **SemBias analogy test (440 instances):** GN-GloVe chooses the gender-definition pair 97.7% of the time, compared with 80.2% for GloVe and 84.1% for Hard-GloVe. On a 40-instance subset built from pairs not used as seeds, the scores are 75%, 57.5% and 25%.
- **Standard benchmarks:** Similarity scores rise, for example RW from 37.3 to 40.0 and MEN-TR-3k from 72.2 to 74.5. Analogy accuracy drops slightly, with Google going from 70.8 to 68.9 and MSR from 45.8 to 43.7.
- **Coreference (Lee et al., 2017 end-to-end model):** OntoNotes 5.0 F1 stays close to the baseline (66.2 vs. 66.5). On WinoBias, the gap between PRO and ANTI subsets shrinks from 30.2 to 20.5, and to 16.1 when only w(a) is used. Hard-GloVe reaches 15.7.
- **Resource:** Code and data are released at github.com/uclanlp/gn_glove.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

167 papers of our library cite it (of the 140884 whose bibliographies we read); 25 of them have a page here, 142 not yet.

- [A Survey on Bias and Fairness in Machine Learning](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/)
- [Language (Technology) is Power: A Critical Survey of “Bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://md.research.elamur.ai/free/en/papers/sun-2019-mitigating-gender-bias-natural-language/)
- [Gender Bias in Contextualized Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
- [Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection](https://md.research.elamur.ai/free/en/papers/ravfogel-2020-null-it-out-guarding-protected/)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://md.research.elamur.ai/free/en/papers/bordia-2019-identifying-reducing-gender-bias-word/)
- [Toxicity in chatgpt: Analyzing persona-assigned language models](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
- [Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/savoldi-2021-gender-bias-machine-translation/)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
- [Towards Debiasing Sentence Representations](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/)
- [Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual](https://md.research.elamur.ai/free/en/papers/he-2019-unlearn-dataset-bias-natural-language/)
- [Societal Biases in Language Generation: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/)
- [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
- [Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
- [Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
- [Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts](https://md.research.elamur.ai/free/en/papers/guo-2022-auto-debias-debiasing-masked-language/)
- [Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/)
- [Towards Controllable Biases in Language Generation](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/)
- [Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cao-2020-gender-inclusive-coreference-resolution/)
- [Intrinsic Bias Metrics Do Not Correlate with Application Bias](https://md.research.elamur.ai/free/en/papers/tarrant-2021-intrinsic-bias-metrics-do-not/)
- [Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/)
- [The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses](https://md.research.elamur.ai/free/en/papers/alhafni-2022-arabic-parallel-gender-corpus-2/)
- [The User-Aware Arabic Gender Rewriter](https://md.research.elamur.ai/free/en/papers/alhafni-2023-user-aware-arabic-gender-rewriter/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2018learning,
  title = {{Learning Gender-Neutral Word Embeddings}},
  author = {Zhao, Jieyu and Zhou, Yichao and Li, Zeyu and Wang, Wei and Chang, Kai-Wei},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1521},
  url = {https://doi.org/10.18653/v1/d18-1521}
}
```

- [← previous Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)
- [next → Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://md.research.elamur.ai/free/en/papers/liu-2018-jointly-multiple-events-extraction-attention/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/)

## References

38 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2018. Linear algebraic structure of word senses, with applications to polysemy. Transactions of the Association of Computational Linguistics, 6:483–495. [find](https://scholar.google.com/scholar?q=Sanjeev+Arora%2C+Yuanzhi+Li%2C+Yingyu+Liang%2C+Tengyu+Ma%2C+and+Andrej+Risteski%2E+2018%2E+Linear+algebraic+structure+of+word+senses%2C+with+applications+to+polysemy%2E+Transactions+of+the+Association+of+Computationa)
2. Sudie E Back, Rebecca L Payne, Annie N Simpson, and Kathleen T Brady. 2010. Gender and prescription opioids: Findings from the national survey on drug use and health. Addictive behaviors, 35(11):1001–1007. [find](https://scholar.google.com/scholar?q=Sudie+E+Back%2C+Rebecca+L+Payne%2C+Annie+N+Simpson%2C+and+Kathleen+T+Brady%2E+2010%2E+Gender+and+prescription+opioids%3A+Findings+from+the+national+survey+on+drug+use+and+health%2E+Addictive+behaviors%2C+35%2811%29%3A1001%E2%80%93)
3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. [find](https://scholar.google.com/scholar?q=Dzmitry+Bahdanau%2C+Kyunghyun+Cho%2C+and+Yoshua+Bengio%2E+2014%2E+Neural+machine+translation+by+jointly+learning+to+align+and+translate%2E+arXiv+preprint+arXiv%3A1409%2E0473%2E) [our card](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
4. Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems, pages 4349–4357. [find](https://scholar.google.com/scholar?q=Tolga+Bolukbasi%2C+Kai%2DWei+Chang%2C+James+Y+Zou%2C+Venkatesh+Saligrama%2C+and+Adam+T+Kalai%2E+2016%2E+Man+is+to+computer+programmer+as+woman+is+to+homemaker%3F+debiasing+word+embeddings%2E+In+Advances+in+Neural+Infor) [our card](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/)
5. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Samuel+R%2E+Bowman%2C+Gabor+Angeli%2C+Christopher+Potts%2C+and+Christopher+D%2E+Manning%2E+2015%2E+A+large+annotated+corpus+for+learning+natural+language+inference%2E+In+Proceedings+of+the+2015+Conference+on+Empirica)
6. Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012. Distributional semantics in technicolor. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pages 136–145. [find](https://scholar.google.com/scholar?q=Elia+Bruni%2C+Gemma+Boleda%2C+Marco+Baroni%2C+and+Nam%2DKhanh+Tran%2E+2012%2E+Distributional+semantics+in+technicolor%2E+In+Proceedings+of+the+50th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%3A+L) [our card](https://md.research.elamur.ai/free/en/papers/bruni-2012-distributional-semantics-technicolor/)
7. Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183–186. [find](https://scholar.google.com/scholar?q=Aylin+Caliskan%2C+Joanna+J+Bryson%2C+and+Arvind+Narayanan%2E+2017%2E+Semantics+derived+automatically+from+language+corpora+contain+human%2Dlike+biases%2E+Science%2C+356%286334%29%3A183%E2%80%93186%2E)
8. Manaal Faruqui, Jesse Dodge, Sujay K Jauhar, Chris Dyer, Eduard Hovy, and Noah A Smith. 2014. Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166. [find](https://scholar.google.com/scholar?q=Manaal+Faruqui%2C+Jesse+Dodge%2C+Sujay+K+Jauhar%2C+Chris+Dyer%2C+Eduard+Hovy%2C+and+Noah+A+Smith%2E+2014%2E+Retrofitting+word+vectors+to+semantic+lexicons%2E+arXiv+preprint+arXiv%3A1411%2E4166%2E)
9. Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: The concept revisited. In Proceedings of the 10th international conference on World Wide Web, pages 406– 414. [find](https://scholar.google.com/scholar?q=Lev+Finkelstein%2C+Evgeniy+Gabrilovich%2C+Yossi+Matias%2C+Ehud+Rivlin%2C+Zach+Solan%2C+Gadi+Wolfman%2C+and+Eytan+Ruppin%2E+2001%2E+Placing+search+in+context%3A+The+concept+revisited%2E+In+Proceedings+of+the+10th+internat)
10. Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2017. Word embeddings quantify 100 years of gender and ethnic stereotypes. arXiv preprint arXiv:1711.08412. — [arXiv:1711.08412](https://arxiv.org/abs/1711.08412) [find](https://scholar.google.com/scholar?q=Nikhil+Garg%2C+Londa+Schiebinger%2C+Dan+Jurafsky%2C+and+James+Zou%2E+2017%2E+Word+embeddings+quantify+100+years+of+gender+and+ethnic+stereotypes%2E+arXiv+preprint+arXiv%3A1711%2E08412%2E+%E2%80%94+%5BarXiv%3A1711%2E08412%5D%28https%3A%2F%2Far)
11. Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16):E3635–E3644. [find](https://scholar.google.com/scholar?q=Nikhil+Garg%2C+Londa+Schiebinger%2C+Dan+Jurafsky%2C+and+James+Zou%2E+2018%2E+Word+embeddings+quantify+100+years+of+gender+and+ethnic+stereotypes%2E+Proceedings+of+the+National+Academy+of+Sciences%2C+115%2816%29%3AE3635%E2%80%93E)
12. Guy Halawi, Gideon Dror, Evgeniy Gabrilovich, and Yehuda Koren. 2012. Large-scale learning of word relatedness with constraints. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1406– 1414. ACM. [find](https://scholar.google.com/scholar?q=Guy+Halawi%2C+Gideon+Dror%2C+Evgeniy+Gabrilovich%2C+and+Yehuda+Koren%2E+2012%2E+Large%2Dscale+learning+of+word+relatedness+with+constraints%2E+In+Proceedings+of+the+18th+ACM+SIGKDD+international+conference+on+Knowl)
13. David A Jurgens, Peter D Turney, Saif M Mohammad, and Keith J Holyoak. 2012. Semeval-2012 task 2: Measuring degrees of relational similarity. In Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main conference and the shared task, and Volu… [find](https://scholar.google.com/scholar?q=David+A+Jurgens%2C+Peter+D+Turney%2C+Saif+M+Mohammad%2C+and+Keith+J+Holyoak%2E+2012%2E+Semeval%2D2012+task+2%3A+Measuring+degrees+of+relational+similarity%2E+In+Proceedings+of+the+First+Joint+Conference+on+Lexical+an)
14. Matthew Kay, Cynthia Matuszek, and Sean A Munson. 2015. Unequal representation and gender stereotypes in image search results for occupations. In Human Factors in Computing Systems, pages 3819– 3828. [find](https://scholar.google.com/scholar?q=Matthew+Kay%2C+Cynthia+Matuszek%2C+and+Sean+A+Munson%2E+2015%2E+Unequal+representation+and+gender+stereotypes+in+image+search+results+for+occupations%2E+In+Human+Factors+in+Computing+Systems%2C+pages+3819%E2%80%93+3828%2E)
15. Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017. End-to-end neural coreference resolution. EMNLP. [find](https://scholar.google.com/scholar?q=Kenton+Lee%2C+Luheng+He%2C+Mike+Lewis%2C+and+Luke+Zettlemoyer%2E+2017%2E+End%2Dto%2Dend+neural+coreference+resolution%2E+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/lee-2017-end-end-neural-coreference-resolution/)
16. Omer Levy, Yoav Goldberg, and Ido Dagan. 2015. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3:211–225. [find](https://scholar.google.com/scholar?q=Omer+Levy%2C+Yoav+Goldberg%2C+and+Ido+Dagan%2E+2015%2E+Improving+distributional+similarity+with+lessons+learned+from+word+embeddings%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+3%3A211%E2%80%93225%2E)
17. Thang Luong, Richard Socher, and Christopher Manning. 2013. Better word representations with recursive neural networks for morphology. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning, pages 104–113. [find](https://scholar.google.com/scholar?q=Thang+Luong%2C+Richard+Socher%2C+and+Christopher+Manning%2E+2013%2E+Better+word+representations+with+recursive+neural+networks+for+morphology%2E+In+Proceedings+of+the+Seventeenth+Conference+on+Computational+Nat) [our card](https://md.research.elamur.ai/free/en/papers/luong-2013-better-word-representations-recursive-neural/)
18. Anna C McFadden, George E Marsh, Barrie Jo Price, and Yunhan Hwang. 1992. A study of race and gender bias in the punishment of school children. Education and treatment of children, pages 140–146. [find](https://scholar.google.com/scholar?q=Anna+C+McFadden%2C+George+E+Marsh%2C+Barrie+Jo+Price%2C+and+Yunhan+Hwang%2E+1992%2E+A+study+of+race+and+gender+bias+in+the+punishment+of+school+children%2E+Education+and+treatment+of+children%2C+pages+140%E2%80%93146%2E)
19. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Kai+Chen%2C+Greg+Corrado%2C+and+Jeffrey+Dean%2E+2013a%2E+Efficient+estimation+of+word+representations+in+vector+space%2E+arXiv+preprint+arXiv%3A1301%2E3781%2E)
20. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111–3119. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Ilya+Sutskever%2C+Kai+Chen%2C+Greg+S+Corrado%2C+and+Jeff+Dean%2E+2013b%2E+Distributed+representations+of+words+and+phrases+and+their+compositionality%2E+In+Advances+in+neural+information+processing)
21. Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013c. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746–751. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Wen%2Dtau+Yih%2C+and+Geoffrey+Zweig%2E+2013c%2E+Linguistic+regularities+in+continuous+space+word+representations%2E+In+Proceedings+of+the+2013+Conference+of+the+North+American+Chapter+of+the+Asso) [our card](https://md.research.elamur.ai/free/en/papers/mikolov-2013-linguistic-regularities-continuous-space-word/)
22. George Miller and Christiane Fellbaum. 1998. Wordnet: An electronic lexical database. [find](https://scholar.google.com/scholar?q=George+Miller+and+Christiane+Fellbaum%2E+1998%2E+Wordnet%3A+An+electronic+lexical+database%2E)
23. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+Manning%2E+2014%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+Proceedings+of+the+2014+conference+on+empirical+methods+in+natural+language+process)
24. Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365. [find](https://scholar.google.com/scholar?q=Matthew+E+Peters%2C+Mark+Neumann%2C+Mohit+Iyyer%2C+Matt+Gardner%2C+Christopher+Clark%2C+Kenton+Lee%2C+and+Luke+Zettlemoyer%2E+2018%2E+Deep+contextualized+word+representations%2E+arXiv+preprint+arXiv%3A1802%2E05365%2E)
25. Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, and Shaul Markovitch. 2011. A word at a time: computing word relatedness using temporal semantic analysis. In Proceedings of the 20th international conference on World wide web, pages 337–346. [find](https://scholar.google.com/scholar?q=Kira+Radinsky%2C+Eugene+Agichtein%2C+Evgeniy+Gabrilovich%2C+and+Shaul+Markovitch%2E+2011%2E+A+word+at+a+time%3A+computing+word+relatedness+using+temporal+semantic+analysis%2E+In+Proceedings+of+the+20th+internationa)
26. Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1753– 1762. [find](https://scholar.google.com/scholar?q=Daojian+Zeng%2C+Kang+Liu%2C+Yubo+Chen%2C+and+Jun+Zhao%2E+2015%2E+Distant+supervision+for+relation+extraction+via+piecewise+convolutional+neural+networks%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Metho)
27. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457. [find](https://scholar.google.com/scholar?q=Jieyu+Zhao%2C+Tianlu+Wang%2C+Mark+Yatskar%2C+Vicente+Ordonez%2C+and+Kai%2DWei+Chang%2E+2017%2E+Men+also+like+shopping%3A+Reducing+gender+bias+amplification+using+corpus%2Dlevel+constraints%2E+arXiv+preprint+arXiv%3A1707%2E09) [our card](https://md.research.elamur.ai/free/en/papers/zhao-2017-men-also-like-shopping-reducing/)
28. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018. Gender bias in coreference resolution: Evaluation and debiasing methods. NAACL. [find](https://scholar.google.com/scholar?q=Jieyu+Zhao%2C+Tianlu+Wang%2C+Mark+Yatskar%2C+Vicente+Ordonez%2C+and+Kai%2DWei+Chang%2E+2018%2E+Gender+bias+in+coreference+resolution%3A+Evaluation+and+debiasing+methods%2E+NAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/)
29. Herbert Rubenstein and John B Goodenough. 1965. Contextual correlates of synonymy. Communications of the ACM, 8(10):627–633. [find](https://scholar.google.com/scholar?q=Herbert+Rubenstein+and+John+B+Goodenough%2E+1965%2E+Contextual+correlates+of+synonymy%2E+Communications+of+the+ACM%2C+8%2810%29%3A627%E2%80%93633%2E)
30. Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017. Social bias in elicited natural language inferences. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pages 74–79. [find](https://scholar.google.com/scholar?q=Rachel+Rudinger%2C+Chandler+May%2C+and+Benjamin+Van+Durme%2E+2017%2E+Social+bias+in+elicited+natural+language+inferences%2E+In+Proceedings+of+the+First+ACL+Workshop+on+Ethics+in+Natural+Language+Processing%2C+pag) [our card](https://md.research.elamur.ai/free/en/papers/rudinger-2017-social-bias-elicited-natural-language/)
31. Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018. Gender bias in coreference resolution. In NAACL. [find](https://scholar.google.com/scholar?q=Rachel+Rudinger%2C+Jason+Naradowsky%2C+Brian+Leonard%2C+and+Benjamin+Van+Durme%2E+2018%2E+Gender+bias+in+coreference+resolution%2E+In+NAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/)
32. Cicero dos Santos and Maira Gatti. 2014. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 69–78. [find](https://scholar.google.com/scholar?q=Cicero+dos+Santos+and+Maira+Gatti%2E+2014%2E+Deep+convolutional+neural+networks+for+sentiment+analysis+of+short+texts%2E+In+Proceedings+of+COLING+2014%2C+the+25th+International+Conference+on+Computational+Lin) [our card](https://md.research.elamur.ai/free/en/papers/santos-2014-deep-convolutional-neural-networks-sentiment/)
33. Simon Šuster, Ivan Titov, and Gertjan Van Noord. 2016. Bilingual learning of multi-sense embeddings with discrete autoencoders. arXiv preprint arXiv:1603.09128. [find](https://scholar.google.com/scholar?q=Simon+S%CC%8Custer%2C+Ivan+Titov%2C+and+Gertjan+Van+Noord%2E+2016%2E+Bilingual+learning+of+multi%2Dsense+embeddings+with+discrete+autoencoders%2E+arXiv+preprint+arXiv%3A1603%2E09128%2E)
34. Latanya Sweeney. 2013. Discrimination in online ad delivery. Queue, 11(3):10. [find](https://scholar.google.com/scholar?q=Latanya+Sweeney%2E+2013%2E+Discrimination+in+online+ad+delivery%2E+Queue%2C+11%283%29%3A10%2E)
35. Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, and James Zou. 2017. Beyond bilingual: Multi-sense word embeddings using multilingual context. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 101–110. [find](https://scholar.google.com/scholar?q=Shyam+Upadhyay%2C+Kai%2DWei+Chang%2C+Matt+Taddy%2C+Adam+Kalai%2C+and+James+Zou%2E+2017%2E+Beyond+bilingual%3A+Multi%2Dsense+word+embeddings+using+multilingual+context%2E+In+Proceedings+of+the+2nd+Workshop+on+Representati)
36. Byron C Wallace and Michael J Paul. 2016. jerk or judgemental? patient perceptions of male versus female physicians in online reviews. [find](https://scholar.google.com/scholar?q=Byron+C+Wallace+and+Michael+J+Paul%2E+2016%2E+jerk+or+judgemental%3F+patient+perceptions+of+male+versus+female+physicians+in+online+reviews%2E)
37. Ralph Weischedel, Sameer Pradhan, Lance Ramshaw, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Nianwen Xue, Martha Palmer, Jena D Hwang, Claire Bonial, et al. 2012. Ontonotes release 5.0. [find](https://scholar.google.com/scholar?q=Ralph+Weischedel%2C+Sameer+Pradhan%2C+Lance+Ramshaw%2C+Jeff+Kaufman%2C+Michelle+Franchini%2C+Mohammed+El%2DBachouti%2C+Nianwen+Xue%2C+Martha+Palmer%2C+Jena+D+Hwang%2C+Claire+Bonial%2C+et+al%2E+2012%2E+Ontonotes+release+5%2E0%2E)
38. Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. In Advances in Neural Information Processing Systems, pages 2925–2934. [find](https://scholar.google.com/scholar?q=Sirui+Yao+and+Bert+Huang%2E+2017%2E+Beyond+parity%3A+Fairness+objectives+for+collaborative+filtering%2E+In+Advances+in+Neural+Information+Processing+Systems%2C+pages+2925%E2%80%932934%2E)