Papers by Ana Sabina Uban
Automatic Discrimination between Inherited and Borrowed Latin Words in Romance Languages (2021.findings-emnlp)
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| Challenge: | Existing approaches to discriminate between inherited and borrowed Latin words have been used to investigate the problem of automatic discrimination between a language's sound shifts. |
| Approach: | They propose a new dataset to investigate the problem of automatically discriminating between inherited and borrowed Latin words in Romance languages. |
| Outcome: | The proposed model can automatically discriminate between inherited and borrowed Latin words on two versions of the dataset, orthographic and phonetic. |
Friend or Foe? A Computational Investigation of Semantic False Friends across Romance Languages (2025.emnlp-main)
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| Challenge: | lexical divergence between cognate and borrowings is studied in the five Romance languages. |
| Approach: | They propose to use etymological dictionaries to extract deceptive cognates and borrowings automatically based on usage and freely publish the lexicon of obtained true and deceptives in every Romance language pair. |
| Outcome: | The proposed algorithms are based on the most complete and reliable dataset of cognate words based etymological dictionaries for the five main Romance languages. |
Pater Incertus? There Is a Solution: Automatic Discrimination between Cognates and Borrowings for Romance Languages (2024.lrec-main)
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Liviu P. Dinu, Ana Sabina Uban, Ioan-Bogdan Iordache, Alina Maria Cristea, Simona Georgescu, Laurentiu Zoicas
| Challenge: | Existing methods for discriminating between cognates and borrowings are difficult, but they provide a deeper insight into the history of a language and allow for a better characterization of language relatedness. |
| Approach: | They propose a computational approach for discriminating between cognates and borrowings based on a comprehensive database of Romance cognates. |
| Outcome: | The proposed approach is the most comprehensive in terms of covered languages. |
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media (2022.lrec-1)
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| Challenge: | Mental disorders are an important and pervasive public health issue. |
| Approach: | They propose to use linguistic features to improve mental disorder detection . they propose to apply multi-aspect transfer learning to detecting disorders from social media . |
| Outcome: | The proposed methods can be used to improve mental disorder detection in the context of data scarcity and understanding the overlapping symptoms between disorders. |
A Computational Exploration of Pejorative Language in Social Media (2021.findings-emnlp)
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| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
| Approach: | They propose to automatically disambiguate pejorative usage in social media . they leverage online dictionaries to build a multilingual lexicon of pejorativ terms . |
| Outcome: | The proposed model can automatically disambiguate pejorative usage in social media posts . the proposed model is based on dictionaries and tweets . |
Studying the Evolution of Scientific Topics and their Relationships (2021.findings-acl)
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| Challenge: | a study of scientific topics and their evolution through time is proposed . we analyze scientific texts published in the field of computational linguistics . |
| Approach: | They propose a multidimensional approach to studying scientific topics through time and their relationships between them. |
| Outcome: | The proposed model analyzes scientific texts published in the ACL Anthology and compares them with case studies to understand how topics evolve and disappear over time. |
Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange (2022.lrec-1)
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| Challenge: | a new data set is used to extract information related to one company . a model that is based on previous information about transactions is not enough . |
| Approach: | They use a Romanian financial news website to extract only information related to one company . they use lexicon-based Vader tool, Financial BERT and Transformer-based models . |
| Outcome: | The proposed model shows that the extracted sentiment scores correlate with stock closing prices . the proposed model is based on data from a Romanian financial news website . |
Automatically Building a Multilingual Lexicon of False Friends With No Supervision (2020.lrec-1)
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| Challenge: | a method to detect false friends from cognates is developed . cognates are words in genetically related languages with a common proto-word . in some cases, cognates have diverged from the common etymon and their meanings became different from each other. |
| Approach: | They propose an automatic method to detect false friends from a set of cognates . cognates are words in genetically related languages which derive from etymons . authors propose a measure of "falseness" of a false friends pair based on cross-lingual word embeddings based in the system . |
| Outcome: | The proposed method can be extended to any language pair, with monolingual corpora and a bilingual dictionary. |