issue #53 update publications list and standardize citation format

Author: georgeslabreche

README.md

@@ -1,6 +1,7 @@
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7734906.svg)](https://doi.org/10.5281/zenodo.7734906)
 
 ## What is CyrTranslit?
+
 A Python package for bi-directional transliteration of Cyrillic script to Latin script and vice versa.
 
 By default, transliterates for the Serbian language. A language flag can be set in order to transliterate to and from Belarusian, Bulgarian, Greek, Montenegrin, Macedonian, Mongolian, Russian, Serbian, Tajik, and Ukrainian.
@@ -10,9 +11,11 @@ By default, transliterates for the Serbian language. A language flag can be set
 **Note:** Development of v1.2.0 is ongoing. Not yet released.
 
 ## What is transliteration?
+
 Transliteration is the conversion of a text from one script to another. For instance, a Latin alphabet transliteration of the Serbian phrase _"Мој ховеркрафт је пун јегуља"_ is _"Moj hoverkraft je pun jegulja"_.
 
 ## Citation
+
 A citation would be much appreciated if you use CyrTranslit in a research publication:
 
 [Georges Labrèche. (2023). CyrTranslit (v1.1.1). Zenodo. https://doi.org/10.5281/zenodo.7734906](https://doi.org/10.5281/zenodo.7734906)
@@ -36,20 +39,74 @@ BibTex entry:
 }
 ```
 
-## Supporting research
+
+## Advancing research
+
 CyrTranslit is actively used as a reliable tool to advance research! Here's an incomplete list of publications for research projects that have relied on CyrTranslit:
-- Ljajić, Adela & Prodanović, Nikola & Medvecki, Darija & Bašaragin, Bojana & Mitrović, Jelena. (2022). "[Topic Modeling Technique on Covid19 Tweets in Serbian](https://www.researchgate.net/publication/364302202_Topic_Modeling_Technique_on_Covid19_Tweets_in_Serbian)," in 12th International Conference on Information Society and Technology (ICIST), Kopaonik, Serbia.
-- Mussylmanbay, Meiirgali. (2022). "[Addresses Standardization and Geocoding using Natural Language Processing](https://nur.nu.edu.kz/handle/123456789/6705)," Nazarbayev University, Kazakhstan.
-- Jokic, Danka & Stanković, Ranka & Krstev, Cvetana & Šandrih Todorović, Branislava. (2021). "[A Twitter Corpus and Lexicon for Abusive Speech Detection in Serbian](https://drops.dagstuhl.de/opus/volltexte/2021/14549/)," in 3rd Conference on Language, Data and Knowledge (LDK 2021). 10.4230/OASIcs.LDK.2021.13. 
-- Lakew, Surafel Melaku (2020). "[Thesis Multilingual Neural Machine Translation for Low Resource Languages](https://surafelml.github.io/phd-thesis/)," University of Trento, Italy.
-- Filo, Denis. (2020). "[Neuronový strojový překlad pro jazykové páry s malým množstvím trénovacích dat: Low-Resource Neural Machine Translation](https://www.fit.vut.cz/study/thesis/23087/.en)," Brno University of Technology, Brno, Czechia.
-- Batanović, Vuk & Nikolic, Bosko. (2019). "[Using Language Technologies to Automate the UNDP Rapid Integrated Assessment Mechanism in Serbian](https://www.researchgate.net/publication/339615659_Using_Language_Technologies_to_Automate_the_UNDP_Rapid_Integrated_Assessment_Mechanism_in_Serbian)," in International Conference on Language Technologies for All: Enabling Linguistic Diversity and Multilingualism Worldwide (LT4All), Paris, France.
-- Brown, J. M. M. & Schmidt, Andreas & Wierzba, Marta (Eds.). (2019). "[Of trees and birds: A Festschrift for Gisbert Fanselow](https://publishup.uni-potsdam.de/opus4-ubp/frontdoor/deliver/index/docId/42654/file/of_trees_and_birds.pdf)," Universitätsverlag Potsdam, Potsdam.
-- Lakew, Surafel Melaku & Erofeeva, Aliia & Federico, Marcello. (2018). "[Neural Machine Translation into Language Varieties](https://aclanthology.org/W18-6316/)," in 3rd Conference on Machine Translation: Research Papers, Brussels, Belgium.
-- Ljajić, Adela & Marovac, Ulfeta. (2018). "[Improving sentiment analysis for twitter data by handling negation rules in the Serbian language](http://www.doiserbia.nb.rs/Article.aspx?ID=1820-02141800013L)," Computer Science and Information Systems. 16. 13-13. 10.2298/CSIS180122013L. 
-- Жабран, И., Кикоть, А., Гафияк, А., Бородина, Е., & Алёшин, С. (2017). "[Developing Q-Orca site backend using various Python programming language libraries](https://www.moderntechno.de/index.php/meit/article/view/meit07-03-021)," Modern Engineering and Innovative Technologies, 3(07-03), 48–53.
+
+### Text Normalization, Unicode Perturbations & Robustness
+
+- Cooper, Portia, Blanco, Eduardo, and Surdeanu, Mihai. (2025). "[The Lies Characters Tell: Utilizing Large Language Models to Normalize Adversarial Unicode Perturbations](https://aclanthology.org/2025.findings-acl.969.pdf)," *Findings of the Association for Computational Linguistics: ACL 2025*.
+
+- Cooper, Portia, Surdeanu, Mihai, and Blanco, Eduardo. (2023). "[Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs](https://aclanthology.org/2023.findings-emnlp.192.pdf)," *Findings of the Association for Computational Linguistics: EMNLP 2023*.
+
+
+### Low-Resource NLP & Machine Translation
+
+- Cvetanović, Aleksa and Tadić, Predrag. (2024). "[Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian](https://arxiv.org/pdf/2404.08617)," arXiv:2404.08617.
+
+- Lakew, Surafel Melaku. (2020). "[Multilingual Neural Machine Translation for Low Resource Languages](https://surafelml.github.io/phd-thesis/)," PhD Thesis, University of Trento.
+
+- Filo, Denis. (2020). "[Neuronový strojový překlad pro jazykové páry s malým množstvím trénovacích dat: Low-Resource Neural Machine Translation](https://www.fit.vut.cz/study/thesis/23087/.en)," Master's Thesis, Brno University of Technology.
+
+- Lakew, Surafel Melaku, Erofeeva, Aliia, and Federico, Marcello. (2018). "[Neural Machine Translation into Language Varieties](https://aclanthology.org/W18-6316/)," *Proceedings of the Third Conference on Machine Translation (WMT 2018)*.
+
+
+### Serbian Language NLP (Topic Modeling, Sentiment, Lexicons, QA, Abuse Detection)
+
+- Medvecki, Darija, Bašaragin, Bojana, Ljajić, Adela, and Milošević, Nikola. (2024). "[Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian](https://doi.org/10.1007/978-3-031-50755-7_16)," *Lecture Notes in Networks and Systems* 872:159-169, Springer.
+
+- Bogdanović, Miloš, Kocić, Jelena, and Stoimenov, Leonid. (2024). "[SRBerta—A Transformer Language Model for Serbian Cyrillic Legal Texts](https://doi.org/10.3390/info15020074)," *Information* 15(2):74.
+
+- Košprdić, Miloš, Prodanović, Nikola, Ljajić, Adela, Bašaragin, Bojana, and Milošević, Nikola. (2024). "[From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts](https://doi.org/10.1016/j.artmed.2024.102970)," *Artificial Intelligence in Medicine* 157:102970.
+
+- Ljajić, Adela, Prodanović, Nikola, Medvecki, Darija, Bašaragin, Bojana, and Mitrović, Jelena. (2022). "[Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling](https://doi.org/10.2196/42261)," *Journal of Medical Internet Research* 24(11):e42261.
+
+- Ljajić, Adela, Prodanović, Nikola, Medvecki, Darija, Bašaragin, Bojana, and Mitrović, Jelena. (2022). "[Topic Modeling Technique on Covid19 Tweets in Serbian](https://www.researchgate.net/publication/364302202_Topic_Modeling_Technique_on_Covid19_Tweets_in_Serbian)," *Proceedings of the 12th International Conference on Information Society and Technology (ICIST 2022)*.
+
+- Jokic, Danka, Stanković, Ranka, Krstev, Cvetana, and Šandrih Todorović, Branislava. (2021). "[A Twitter Corpus and Lexicon for Abusive Speech Detection in Serbian](https://drops.dagstuhl.de/opus/volltexte/2021/14549/)," *Proceedings of the 3rd Conference on Language, Data and Knowledge (LDK 2021)*.
+
+- Batanović, Vuk and Nikolic, Bosko. (2019). "[Using Language Technologies to Automate the UNDP Rapid Integrated Assessment Mechanism in Serbian](https://www.researchgate.net/publication/339615659_Using_Language_Technologies_to_Automate_the_UNDP_Rapid_Integrated_Assessment_Mechanism_in_Serbian)," *Proceedings of the Conference on Language Technologies for All (LT4All)*.
+
+- Ljajić, Adela and Marovac, Ulfeta. (2018). "[Improving sentiment analysis for twitter data by handling negation rules in the Serbian language](http://www.doiserbia.nb.rs/Article.aspx?ID=1820-02141800013L)," *Computer Science and Information Systems* 16(1):13-33.
+
+
+### NLP Applications for Society, Government, and Political Analysis
+
+- Paula, Katrin and Scholz, Nele. (2025). "[Where do regimes rally their supporters? The geographical distribution of pro-government mobilization in Russia from February to April 2022](https://www.sciencedirect.com/science/article/pii/S096262982500068X)," *Political Geography* 116:103277.
+
+
+### Engineering, Software Systems, and Backend Development
+
+- Alyoshin, S.P., Borodina, E.A., Hafiiak, A.M., Zhabran, I.B., and Kikot, A.S. (2019). "[Developing Q-Orca site backend using various Python programming language libraries](https://reposit.nupp.edu.ua/bitstream/PoltNTU/5811/1/ME%26IT_Part%203_P%2048_March%202019_Aleshin_Borodina_Hafiiak_Zhabran_Kikot%20%28pdf.io%29.pdf)," *Modern Engineering and Innovative Technologies* 3(7-3):48-53.
+
+- Жабран, И., Кикоть, А., Гафияк, А., Бородина, Е., and Алёшин, С. (2017). "[Developing Q-Orca site backend using various Python programming language libraries](https://www.moderntechno.de/index.php/meit/article/view/meit07-03-021)," *Modern Engineering and Innovative Technologies* 3(07-03):48-53.
+
+
+### Proceedings, Collections, and Meta-Documents
+
+- Anonymous. (2021). "[Complete Volume: Proceedings of the 3rd Conference on Language, Data and Knowledge (LDK 2021)](http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14535/pdf/oasics-vol093-ldk2021-complete.pdf)," *OASIcs* Vol. 93.
+
+- Brown, J. M. M., Schmidt, Andreas, and Wierzba, Marta (Eds.). (2019). "[Of trees and birds: A Festschrift for Gisbert Fanselow](https://publishup.uni-potsdam.de/opus4-ubp/frontdoor/deliver/index/docId/42654/file/of_trees_and_birds.pdf)," Universitätsverlag Potsdam.
+
+
+### Addresses, Geocoding, and NLP
+
+- Mussylmanbay, Meiirgali. (2022). "[Addresses Standardization and Geocoding using Natural Language Processing](https://nur.nu.edu.kz/handle/123456789/6705)," Master's Thesis, Nazarbayev University.
+
 
 ## How do I install this?
+
 CyrTranslit is [hosted in the Python Package Index (PyPI)](https://pypi.python.org/pypi/cyrtranslit) so it can be installed using pip:
 ```
 python3 -m pip install cyrtranslit         # latest version
@@ -58,6 +115,7 @@ python3 -m pip install cyrtranslit>=1.2.0  # minimum version
 ```
 
 ## What languages are supported?
+
 CyrTranslit currently supports bi-directional transliteration of Belarusian, Bulgarian, Greek, Montenegrin, Macedonian, Mongolian, Russian, Serbian, Tajik, and Ukrainian.
 
 Language codes are based on ISO 639-1 standards. For Serbian, both `sr` (ISO 639-1 language code) and `rs` (ISO 3166-1 country code) are accepted:
@@ -66,11 +124,15 @@ Language codes are based on ISO 639-1 standards. For Serbian, both `sr` (ISO 639
 >>> cyrtranslit.supported()
 ['bg', 'by', 'el', 'me', 'mk', 'mn', 'rs', 'ru', 'sr', 'tj', 'ua']
 ```
+
 ## How do I use this? 
+
 CyrTranslit can be used both programatically and via command line interface.
 
 ### Programmatically
+
 #### Belarusian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Прывітанне, свет!", "by")
@@ -80,6 +142,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Bulgarian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Съединението прави силата!", "bg")
@@ -89,6 +152,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Greek
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Το χόβερκραφτ μου είναι γεμάτο χέλια", "el")
@@ -98,6 +162,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Montenegrin
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Република", "me")
@@ -107,6 +172,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Macedonian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Моето летачко возило е полно со јагули", "mk")
@@ -116,6 +182,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Mongolian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Амрагаа Сүнжидмаагаа гэсээр ирлээ дээ хө-хө-хө", "mn")
@@ -125,6 +192,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Russian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Моё судно на воздушной подушке полно угрей", "ru")
@@ -134,6 +202,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Serbian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Мој ховеркрафт је пун јегуља")
@@ -143,6 +212,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Tajik
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Ман мактуб навишта истодам", "tj")
@@ -152,6 +222,7 @@ CyrTranslit can be used both programatically and via command line interface.
 ```
 
 #### Ukrainian
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("Під лежачий камінь вода не тече", "ua")
@@ -188,6 +259,7 @@ CyrTranslit supports Cyrillic characters with grave accents used in Macedonian a
 #### Usage Examples
 
 **Default behavior (accents stripped):**
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("ѝ је", "mk")
@@ -199,6 +271,7 @@ CyrTranslit supports Cyrillic characters with grave accents used in Macedonian a
 ```
 
 **With accents preserved:**
+
 ```python
 >>> import cyrtranslit
 >>> cyrtranslit.to_latin("ѝ је", "mk", preserve_accents=True)
@@ -212,6 +285,7 @@ CyrTranslit supports Cyrillic characters with grave accents used in Macedonian a
 ```
 
 **Command-line usage:**
+
 ```bash
 # Default (accents stripped)
 $ echo "ѝ је" | cyrtranslit -l mk
@@ -223,6 +297,7 @@ $ echo "ѝ је" | cyrtranslit -l mk --preserve-accents
 ```
 
 ## Command Line Interface
+
 Sample command line call to transliterate a Russian text file:
 ```bash
 $ cyrtranslit -l RU -i tests/ru.txt -o tests/output.txt
@@ -241,6 +316,7 @@ $ echo 'Moj hoverkraft je pun jegulja' | cyrtranslit -l sr
 ```
 
 ### File Encodings
+
 By default, input files are expected to be UTF-8. For files with different encodings, use the `-e/--encoding` parameter:
 
 ```bash
@@ -262,6 +338,7 @@ Try CyrTranslit by running it directly on the Python command line interface, e.g
 
 
 ## How can I contribute?
+
 Include support for other Cyrillic script alphabets. Follow these steps in order to do so:
 
 1. Create a new transliteration mapping file in the **[mapping/](https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/cyrtranslit/mapping/)** directory (using the language code as the filename, e.g., `xx.py`) and reference to it in the _**[TRANSLIT\_DICT](https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/cyrtranslit/mapping/__init__.py)**_ dictionary in **mapping/\_\_init\_\_.py**. If the language uses accented characters (like Macedonian and Bulgarian), create separate accented dictionaries (e.g., `XX_CYR_TO_LAT_ACCENTED_DICT`) following the pattern in **[mk.py](https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/cyrtranslit/mapping/mk.py)** or **[bg.py](https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/cyrtranslit/mapping/bg.py)**.
@@ -272,10 +349,12 @@ Include support for other Cyrillic script alphabets. Follow these steps in order
 6. List yourself as one of the contributors.
 
 Before tagging a release version and deploying to [PyPI](https://pypi.org/):
+
 1. Update the `version` and `download_url` properties in [setup.py](https://github.com/opendatakosovo/cyrillic-transliteration/blob/master/setup.py).
 2. [Reserve a Zenodo DOI](https://cassgvp.github.io/github-for-collaborative-documentation/docs/tut/6-Zenodo-integration.html) for the release and update this readme's Zenodo badge and [citation instructions](https://github.com/opendatakosovo/cyrillic-transliteration#citation).
 
 A big thank you to everyone who contributed:
+
 - Bulgarian 🇧🇬: [@Syndamia](https://github.com/Syndamia) and [@Sparkycz](https://github.com/Sparkycz).
 - Russian 🇷🇺: [@ratijas](https://github.com/ratijas) and [@rominf](https://github.com/rominf).
 - Tajik 🇹🇯: [@diejani](https://github.com/diejani).