[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/aclbulk-2024-liebeskind-linguistics-practice-case-study-offensive/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/) / From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew

# From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew

**Authors:** [Chaya Liebeskind](https://md.mova.elamur.ai/free/en/docs/papers/authors/chaya-liebeskind/), [Marina Litvak](https://md.mova.elamur.ai/free/en/docs/papers/authors/marina-litvak/), [Natalia Vanetik](https://md.mova.elamur.ai/free/en/docs/papers/authors/natalia-vanetik/) · **Year:** 2024 · **Venue:** Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024) **Link:** [aclanthology.org/2024.woah-1.8](https://aclanthology.org/2024.woah-1.8/) · library `aclbulk-2024-liebeskind-linguistics-practice-case-study-offensive` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**1** citations](https://openalex.org/works?filter=cites:W4401042015) openalex · 2026-10-18[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2024-liebeskind-linguistics-practice-case-study-offensive/) of the 140673 papers whose bibliographies we read[Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)](https://aclanthology.org/volumes/2024.woah-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2024.woah-1.8/)
- [PDF](https://aclanthology.org/2024.woah-1.8.pdf)
- [DOI 10.18653/v1/2024.woah-1.8](https://doi.org/10.18653/v1/2024.woah-1.8)
- [OpenAlex](https://openalex.org/W4401042015)

**Authors (3)**

1. [Chaya Liebeskind](https://openalex.org/A5048937508) [ORCID](https://orcid.org/0000-0003-0476-3796) · h 10 · 338 citations · 113 works
2. [Marina Litvak](https://openalex.org/A5031572202) [ORCID](https://orcid.org/0000-0003-3044-3681) · h 15 · 1045 citations · 115 works
3. [Natalia Vanetik](https://openalex.org/A5080435987) [ORCID](https://orcid.org/0000-0002-4939-1415) · h 11 · 633 citations · 104 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper applies a Hebrew translation of the explicit-offence taxonomy of Lewandowska-Tomaszczyk et al. (2023) to Hebrew social-media text. Candidate offensive terms were found by co-occurrence (Dice coefficient) from 67 seed terms, and 683 were chosen by hand. Tweets were then extracted with those terms, and 450 texts (9 aspect files of 50 each) were annotated by two native speakers, with a third for disagreements. Cohen's kappa was 0.49 for target presence, 0.84 for target type, 0.73 for insult and hate speech severity (0.66 for discrediting, 0.97 for threat), 0.63 for vulgarity and 0.68 for the aspect list. Binary classifiers (XGBoost on AlephBERT sentence vectors) reach F1 0.699 for presence against a majority of 0.518. Vulgarity and severity are hard, and the multi-class aspect task gives accuracy 0.488 against a majority of 0.545. Fine-tuned multilingual BERT and Hebrew BERT do worse than the XGBoost model. The authors conclude that fixed taxonomies and word-based classes do not capture Hebrew offence well at the lower levels.

## Ideas

- I1. The taxonomy as a decision chain in Rust: presence, then target, then target type, then severity, then vulgarity, then aspect. Each node is a rule over parsed features and stores its reason, so an output can say which level decided the label.
- I2. Seed-based lexicon growth: from a short list of offensive words, take the 100 nearest neighbours by Dice coefficient in an unannotated corpus, then accept candidates by hand and record the rejection rate. This is a reusable Hebrew lexicon build ([8. Lexicon and register](https://md.mova.elamur.ai/free/en/docs/languages/he/08-lexicon-register/)).
- I3. The per-level kappa table is a reference: any Mova rule set for these levels must beat the majority baseline and report kappa against the same two annotators' level. Use the 450-text design (9 aspects, 50 each) as a test layout, after checking the release status of the data.

## For Mova

I1 is a rule design for `prag`. I2 goes into [8. Lexicon and register](https://md.mova.elamur.ai/free/en/docs/languages/he/08-lexicon-register/). I3 is the test plan for [9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/). The offence aspects also overlap with the political and register work in [8. Lexicon and register](https://md.mova.elamur.ai/free/en/docs/languages/he/08-lexicon-register/).

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2024.woah-1.8](https://doi.org/10.18653/v1/2024.woah-1.8)

```
@inproceedings{liebeskind2024linguistics,
  title = {{From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew}},
  author = {Liebeskind, Chaya and Litvak, Marina and Vanetik, Natalia},
  booktitle = {Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)},
  year = {2024},
  doi = {10.18653/v1/2024.woah-1.8},
  url = {https://doi.org/10.18653/v1/2024.woah-1.8}
}
```

- [← previous Transformer-based Hebrew NLP models for Short Answer Scoring in Biology](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2023-schleifer-transformer-based-hebrew-nlp-models/)
- [next → OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-otobert-identifying-suffixed-verbal-forms/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

## References

39 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Dana Alsagheer, Hadi Mansourifar, and Weidong Shi. 2022. Counter hate speech in social media: A survey. arXiv preprint arXiv:2203.03584. [↗](https://arxiv.org/abs/2203.03584)
2. Anna C Belkina, Christopher O Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, and Jennifer E Snyder-Cappione. 2019. Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature communications, 10(1):5415. [find](https://scholar.google.com/scholar?q=Anna+C+Belkina%2C+Christopher+O+Ciccolella%2C+Rina+Anno%2C+Richard+Halpert%2C+Josef+Spidlen%2C+and+Jennifer+E+Snyder%2DCappione%2E+2019%2E+Automated+optimized+parameters+for+t%2Ddistributed+stochastic+neighbor+embeddin)
3. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the association for computational linguistics, 5:135–146. [↗](https://doi.org/10.1162/tacl_a_00051)
4. Çağrı Çöltekin. 2020. A corpus of turkish offensive language on social media. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6174–6184. [find](https://scholar.google.com/scholar?q=%C3%87ag%CC%86r%C4%B1+%C3%87%C3%B6ltekin%2E+2020%2E+A+corpus+of+turkish+offensive+language+on+social+media%2E+In+Proceedings+of+the+Twelfth+Language+Resources+and+Evaluation+Conference%2C+pages+6174%E2%80%936184%2E)
5. Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, et al. 2015. Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4):1–4. [find](https://scholar.google.com/scholar?q=Tianqi+Chen%2C+Tong+He%2C+Michael+Benesty%2C+Vadim+Khotilovich%2C+Yuan+Tang%2C+Hyunsu+Cho%2C+Kailong+Chen%2C+et+al%2E+2015%2E+Xgboost%3A+extreme+gradient+boosting%2E+R+package+version+0%2E4%2D2%2C+1%284%29%3A1%E2%80%934%2E)
6. Patricia Chiril, Farah Benamara, Véronique Moriceau, Marlene Coulomb-Gully, and Abhishek Kumar. 2019. Multilingual and multitarget hate speech detection in tweets. In Conférence sur le Traitement Automatique des Langues Naturelles (TALN-PFIA 2019), pages 351–360. ATALA. [find](https://scholar.google.com/scholar?q=Patricia+Chiril%2C+Farah+Benamara%2C+V%C3%A9ronique+Moriceau%2C+Marlene+Coulomb%2DGully%2C+and+Abhishek+Kumar%2E+2019%2E+Multilingual+and+multitarget+hate+speech+detection+in+tweets%2E+In+Conf%C3%A9rence+sur+le+Traitement+Auto)
7. Avihay Chriqui and Inbal Yahav. 2021. Hebert and hebemo: a hebrew bert model and a tool for polarity analysis and emotion recognition. arXiv preprint arXiv:2102.01909. [↗](https://arxiv.org/abs/2102.01909) [our card](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2021-chriqui-hebert-hebemo-hebrew-bert-model/)
8. Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37–46. [↗](https://doi.org/10.1177/001316446002000104)
9. Nagham Hamad, Mustafa Jarrar, Mohammad Khalilia, and Nadim Nashif. 2023. Offensive hebrew corpus and detection using bert. arXiv preprint arXiv:2309.02724. [↗](https://arxiv.org/abs/2309.02724) [our card](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-hamad-offensive-hebrew-corpus-detection-using/)
10. Michael Haugh and Valeria Sinkeviciute. 2019. Offence and conflict talk. The Routledge handbook of language in conflict, pages 196–214. [find](https://scholar.google.com/scholar?q=Michael+Haugh+and+Valeria+Sinkeviciute%2E+2019%2E+Offence+and+conflict+talk%2E+The+Routledge+handbook+of+language+in+conflict%2C+pages+196%E2%80%93214%2E)
11. HuggingFace. 2024. XLM-RoBERTa-Multilingual-Hate-Speech-Detection-New: A Pretrained Model for Multilingual Hate Speech Detection. [https://huggingface.co/christinacdl/XLM_](https://huggingface.co/christinacdl/XLM_) RoBERTa-Multilingual-Hate-Speech-Detection-New. Accessed: April 23, 2024. [↗](https://huggingface.co/christinacdl/XLM_)
12. Jan-Christoph Klie, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, and Iryna Gurevych. 2018. The inception platform: Machine-assisted and knowledge-oriented interactive annotation. In proceedings of the 27th international conference on computational linguistics: system demonstrations, pages 5–9. [find](https://scholar.google.com/scholar?q=Jan%2DChristoph+Klie%2C+Michael+Bugert%2C+Beto+Boullosa%2C+Richard+Eckart+de+Castilho%2C+and+Iryna+Gurevych%2E+2018%2E+The+inception+platform%3A+Machine%2Dassisted+and+knowledge%2Doriented+interactive+annotation%2E+In+proc)
13. SV Kogilavani, S Malliga, KR Jaiabinaya, M Malini, and M Manisha Kokila. 2021. Characterization and mechanical properties of offensive language taxonomy and detection techniques. Materials Today: Proceedings. [find](https://scholar.google.com/scholar?q=SV+Kogilavani%2C+S+Malliga%2C+KR+Jaiabinaya%2C+M+Malini%2C+and+M+Manisha+Kokila%2E+2021%2E+Characterization+and+mechanical+properties+of+offensive+language+taxonomy+and+detection+techniques%2E+Materials+Today%3A+Proc)
14. Barbara Lewandowska-Tomaszczyk. 2023. A simplified taxonomy of offensive language (sol) for computational applications. Konin Language Studies, 10(3):213–227. [find](https://scholar.google.com/scholar?q=Barbara+Lewandowska%2DTomaszczyk%2E+2023%2E+A+simplified+taxonomy+of+offensive+language+%28sol%29+for+computational+applications%2E+Konin+Language+Studies%2C+10%283%29%3A213%E2%80%93227%2E)
15. Barbara Lewandowska-Tomaszczyk, Anna B aczkowska, Chaya Liebeskind, Giedre Valunaite Oleskeviciene, and Slavko Žitnik. 2023. An integrated explicit and implicit offensive language taxonomy. Lodz Papers in Pragmatics, 19(1):7–48. [find](https://scholar.google.com/scholar?q=Barbara+Lewandowska%2DTomaszczyk%2C+Anna+B+aczkowska%2C+Chaya+Liebeskind%2C+Giedre+Valunaite+Oleskeviciene%2C+and+Slavko+%C5%BDitnik%2E+2023%2E+An+integrated+explicit+and+implicit+offensive+language+taxonomy%2E+Lodz+Paper)
16. Barbara Lewandowska-Tomaszczyk, Slavko Žitnik, Chaya Liebeskind, Giedrė Valūnaitė-Oleškevičienė, Anna B aczkowska, Paul A Wilson, Marcin Trojszczak, Ivana Brač, Lobel Filipić, Ana Ostroški Anić, et al. 2022. Annotation scheme and evaluation: The case of offensive language. Rasprave. [find](https://scholar.google.com/scholar?q=Barbara+Lewandowska%2DTomaszczyk%2C+Slavko+%C5%BDitnik%2C+Chaya+Liebeskind%2C+Giedre%CC%87+Valu%CC%84naite%CC%87%2DOle%C5%A1kevic%CC%8Ciene%CC%87%2C+Anna+B+aczkowska%2C+Paul+A+Wilson%2C+Marcin+Trojszczak%2C+Ivana+Brac%CC%8C%2C+Lobel+Filipic%CC%81%2C+Ana+Ostro%C5%A1ki+Anic)
17. Matthew Costello and James Hawdon. 2020. Hate speech in online spaces. The Palgrave handbook of international cybercrime and cyberdeviance, pages 1397–1416. [find](https://scholar.google.com/scholar?q=Matthew+Costello+and+James+Hawdon%2E+2020%2E+Hate+speech+in+online+spaces%2E+The+Palgrave+handbook+of+international+cybercrime+and+cyberdeviance%2C+pages+1397%E2%80%931416%2E)
18. Chaya Liebeskind and Shmuel Liebeskind. 2018. Identifying abusive comments in Hebrew Facebook. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE), pages 1–5. IEEE. [find](https://scholar.google.com/scholar?q=Chaya+Liebeskind+and+Shmuel+Liebeskind%2E+2018%2E+Identifying+abusive+comments+in+Hebrew+Facebook%2E+In+2018+IEEE+International+Conference+on+the+Science+of+Electrical+Engineering+in+Israel+%28ICSEE%29%2C+pages+1)
19. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515. [find](https://scholar.google.com/scholar?q=Thomas+Davidson%2C+Dana+Warmsley%2C+Michael+Macy%2C+and+Ingmar+Weber%2E+2017%2E+Automated+hate+speech+detection+and+the+problem+of+offensive+language%2E+In+Proceedings+of+the+international+AAAI+conference+on+web+)
20. Darja Fišer, Tomaž Erjavec, and Nikola Ljubešić. 2017. Legal framework, dataset and annotation schema for socially unacceptable online discourse practices in Slovene. In Proceedings of the first workshop on abusive language online, pages 46–51. [find](https://scholar.google.com/scholar?q=Darja+Fi%C5%A1er%2C+Toma%C5%BE+Erjavec%2C+and+Nikola+Ljube%C5%A1ic%CC%81%2E+2017%2E+Legal+framework%2C+dataset+and+annotation+schema+for+socially+unacceptable+online+discourse+practices+in+Slovene%2E+In+Proceedings+of+the+first+work)
21. Marina Litvak, Natalia Vanetik, Chaya Liebeskind, Omar Hmdia, and Rizek Abu Madeghem. 2022. Offensive language detection in hebrew: can other languages help? In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3715–3723. [find](https://scholar.google.com/scholar?q=Marina+Litvak%2C+Natalia+Vanetik%2C+Chaya+Liebeskind%2C+Omar+Hmdia%2C+and+Rizek+Abu+Madeghem%2E+2022%2E+Offensive+language+detection+in+hebrew%3A+can+other+languages+help%3F+In+Proceedings+of+the+Thirteenth+Language+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2022-litvak-offensive-language-detection-hebrew-can/)
22. Paula Fortuna, Joao Rocha da Silva, Leo Wanner, Sérgio Nunes, et al. 2019. A hierarchically-labeled Portuguese hate speech dataset. In Proceedings of the third workshop on abusive language online, pages 94–104. [find](https://scholar.google.com/scholar?q=Paula+Fortuna%2C+Joao+Rocha+da+Silva%2C+Leo+Wanner%2C+S%C3%A9rgio+Nunes%2C+et+al%2E+2019%2E+A+hierarchically%2Dlabeled+Portuguese+hate+speech+dataset%2E+In+Proceedings+of+the+third+workshop+on+abusive+language+online%2C+pag)
23. Marina Litvak, Natalia Vanetik, Yaser Nimer, Abdulrhman Skout, and Israel Beer-Sheba. 2021. Offensive language detection in semitic languages. In Multimodal Hate Speech Workshop, volume 2021, pages 7–12. [find](https://scholar.google.com/scholar?q=Marina+Litvak%2C+Natalia+Vanetik%2C+Yaser+Nimer%2C+Abdulrhman+Skout%2C+and+Israel+Beer%2DSheba%2E+2021%2E+Offensive+language+detection+in+semitic+languages%2E+In+Multimodal+Hate+Speech+Workshop%2C+volume+2021%2C+pages+7%E2%80%93)
24. Ping Liu, Wen Li, and Liang Zou. 2019. NULI at SemEval-2019 task 6: Transfer learning for offensive language detection using bidirectional transformers. In Proceedings of the 13th international workshop on semantic evaluation, pages 87–91. [find](https://scholar.google.com/scholar?q=Ping+Liu%2C+Wen+Li%2C+and+Liang+Zou%2E+2019%2E+NULI+at+SemEval%2D2019+task+6%3A+Transfer+learning+for+offensive+language+detection+using+bidirectional+transformers%2E+In+Proceedings+of+the+13th+international+worksh)
25. Hanane Mohaouchane, Asmaa Mourhir, and Nikola S Nikolov. 2019. Detecting offensive language on Arabic social media using deep learning. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pages 466–471. IEEE. [find](https://scholar.google.com/scholar?q=Hanane+Mohaouchane%2C+Asmaa+Mourhir%2C+and+Nikola+S+Nikolov%2E+2019%2E+Detecting+offensive+language+on+Arabic+social+media+using+deep+learning%2E+In+2019+Sixth+International+Conference+on+Social+Networks+Analys)
26. Mahesh Pal. 2005. Random forest classifier for remote sensing classification. International journal of remote sensing, 26(1):217–222. [find](https://scholar.google.com/scholar?q=Mahesh+Pal%2E+2005%2E+Random+forest+classifier+for+remote+sensing+classification%2E+International+journal+of+remote+sensing%2C+26%281%29%3A217%E2%80%93222%2E)
27. Zeses Pitenis, Marcos Zampieri, and Tharindu Ranasinghe. 2020. Offensive language identification in Greek. arXiv preprint arXiv:2003.07459. [↗](https://arxiv.org/abs/2003.07459)
28. Konthala Yasaswini, Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021. IIITT@ DravidianLangTech-EACL2021: Transfer learning for offensive language detection in Dravidian languages. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 187–194. [find](https://scholar.google.com/scholar?q=Konthala+Yasaswini%2C+Karthik+Puranik%2C+Adeep+Hande%2C+Ruba+Priyadharshini%2C+Sajeetha+Thavareesan%2C+and+Bharathi+Raja+Chakravarthi%2E+2021%2E+IIITT%40+DravidianLangTech%2DEACL2021%3A+Transfer+learning+for+offensive+la)
29. Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019a. Predicting the type and target of offensive posts in social media. arXiv preprint arXiv:1902.09666. [↗](https://arxiv.org/abs/1902.09666)
30. Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019b. Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983. [↗](https://arxiv.org/abs/1903.08983)
31. Fabio Poletto, Marco Stranisci, Manuela Sanguinetti, Viviana Patti, Cristina Bosco, et al. 2017. Hate speech annotation: Analysis of an Italian Twitter corpus. In Ceur workshop proceedings, volume 2006, pages 1–6. CEUR-WS. [find](https://scholar.google.com/scholar?q=Fabio+Poletto%2C+Marco+Stranisci%2C+Manuela+Sanguinetti%2C+Viviana+Patti%2C+Cristina+Bosco%2C+et+al%2E+2017%2E+Hate+speech+annotation%3A+Analysis+of+an+Italian+Twitter+corpus%2E+In+Ceur+workshop+proceedings%2C+volume+200)
32. Tharindu Ranasinghe, Marcos Zampieri, and Hansi Hettiarachchi. 2019. Brums at hasoc 2019: Deep learning models for multilingual hate speech and offensive language identification. In FIRE (working notes), pages 199–207. [find](https://scholar.google.com/scholar?q=Tharindu+Ranasinghe%2C+Marcos+Zampieri%2C+and+Hansi+Hettiarachchi%2E+2019%2E+Brums+at+hasoc+2019%3A+Deep+learning+models+for+multilingual+hate+speech+and+offensive+language+identification%2E+In+FIRE+%28working+note)
33. Hinrich Schütze and Jan O Pedersen. 1997. A cooccurrence-based thesaurus and two applications to information retrieval. Information Processing & Management, 33(3):307–318. [find](https://scholar.google.com/scholar?q=Hinrich+Sch%C3%BCtze+and+Jan+O+Pedersen%2E+1997%2E+A+cooccurrence%2Dbased+thesaurus+and+two+applications+to+information+retrieval%2E+Information+Processing+%26+Management%2C+33%283%29%3A307%E2%80%93318%2E)
34. Amit Seker, Elron Bandel, Dan Bareket, Idan Brusilovsky, Refael Shaked Greenfeld, and Reut Tsarfaty. 2021. Alephbert: A hebrew large pre-trained language model to start-off your hebrew nlp application with. arXiv preprint arXiv:2104.04052. [↗](https://arxiv.org/abs/2104.04052) [our card](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2021-seker-alephbert-hebrew-large-pre-trained/)
35. Jonathon Shlens. 2014. A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100. [↗](https://arxiv.org/abs/1404.1100)
36. Gudbjartur Ingi Sigurbergsson and Leon Derczynski. 2019. Offensive language and hate speech detection for Danish. arXiv preprint arXiv:1908.04531. [↗](https://arxiv.org/abs/1908.04531)
37. Frank Smadja, Kathleen R McKeown, and Vasileios Hatzivassiloglou. 1996. Translating collocations for bilingual lexicons: A statistical approach. Computational linguistics, 22(1):1–38. [find](https://scholar.google.com/scholar?q=Frank+Smadja%2C+Kathleen+R+McKeown%2C+and+Vasileios+Hatzivassiloglou%2E+1996%2E+Translating+collocations+for+bilingual+lexicons%3A+A+statistical+approach%2E+Computational+linguistics%2C+22%281%29%3A1%E2%80%9338%2E)
38. Stéphan Tulkens, Lisa Hilte, Elise Lodewyckx, Ben Verhoeven, and Walter Daelemans. 2016. A dictionary-based approach to racism detection in dutch social media. arXiv preprint arXiv:1608.08738. [↗](https://arxiv.org/abs/1608.08738)
39. Raymond E Wright. 1995. Logistic regression. [find](https://scholar.google.com/scholar?q=Raymond+E+Wright%2E+1995%2E+Logistic+regression%2E)