Papers by Liviu P. Dinu
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. |
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. |
Exploring Optimism and Pessimism in Twitter Using Deep Learning (D18-1)
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| Challenge: | Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need it. |
| Approach: | They propose deep learning models to predict optimism and pessimism in Twitter . they also show that a sentiment classifier would not be sufficient for predicting optimism and psi . |
| Outcome: | The proposed models outperform traditional machine learning classifiers on optimism and pessimism in Twitter. |
Automatic Reconstruction of Missing Romanian Cognates and Unattested Latin Words (2020.lrec-1)
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| Challenge: | Existing methods for producing related words are based on sequence labeling . |
| Approach: | They propose a method for producing related words based on sequence labeling . they aim to fill in gaps in incomplete cognate sets in Romance languages with Latin etymology and reconstruct uncertified Latin words. |
| Outcome: | The proposed method fills in gaps in incomplete cognate sets in Romance languages with Latin etymology and reconstructs uncertified Latin words. |
The Myth of Double-Blind Review Revisited: ACL vs. EMNLP (D19-1)
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| Challenge: | a double-blind review system enforces author anonymity during the review period . authors can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP . |
| Approach: | They examine how well deep learning techniques can infer authors of a paper . authors found authors can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP . |
| Outcome: | The proposed method can infer authors with 87% accuracy on ACL and 78% on EMNLP for the top 100 most prolific authors. |
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism (2022.lrec-1)
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| Challenge: | a recent study has established sentiment analysis as an alluring problem, but many feelings are left unexplored. |
| Approach: | They propose a framework to learn the polarity of emotions from Twitter posts . they compare optimism detection with sentiment analysis and hate speech detection . |
| Outcome: | The proposed framework differs between optimistic and pessimistic users on the Optimism/Pessimism Twitter dataset. |
Simulating Language Evolution: a Tool for Historical Linguistics (C18-2)
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| Challenge: | Language change across space and time is one of the main concerns in historical linguistics. |
| Approach: | They propose a web-based tool for word form production that predicts how a word evolves in a target language. |
| Outcome: | The proposed method is language-agnostic and does not use external knowledge except for the training word pairs. |
RED v2: Enhancing RED Dataset for Multi-Label Emotion Detection (2022.lrec-1)
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| Challenge: | RED is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts. |
| Approach: | They propose an open-source extension of RED by adding trust and surprise . they propose two variants of ground truth suitable for multi-label classification and text regression . |
| Outcome: | The proposed model is based on two models with two transformer models, the Romanian BERT and the multilingual XLM-Roberta model, in categorical and regression settings. |
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. |
An Exploratory Analysis of the Relation between Offensive Language and Mental Health (2021.findings-acl)
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| Challenge: | Using computational models, the use of offensive language is pervasive in social media . a popular line of research is the study of machine learning classifiers to identify offensive content online . |
| Approach: | They analyze social media posts written by individuals with depression and those without . they train computational models to compare use of offensive language with depression detection . |
| Outcome: | The proposed models show that offensive language is more frequently used in the samples written by individuals with depression and those showing signs of depression. |
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 . |
Ab Initio: Automatic Latin Proto-word Reconstruction (C18-1)
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| Challenge: | Existing methods for proto-word reconstruction are time-consuming and manual, but few studies have done it . a recent study used cognates to reconstruct ancient languages from their modern counterparts . |
| Approach: | They propose to use Latin proto-words to automate the process of proto-language reconstruction . they leverage information from all modern languages and use conditional random fields for sequence labeling . |
| Outcome: | The proposed method improves on previous results and requires less data . it is based on word forms in multiple Romance languages and on recurrent neural networks . |
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression (2022.lrec-1)
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| Challenge: | a new study explores the relationship between depression and manifestations of happiness in social media . we use Positive-Unlabeled learning paradigm to extract happy moments from social media posts . 264 million people of all ages suffer from depression, according to the u.s. |
| Approach: | They propose a positive-unlabeled learning paradigm to extract happy moments from social media . they use LIWC and keyness information to qualitatively analyze the happy moments . |
| Outcome: | The proposed method extracts happy moments from social media posts of depressed users and controls . it qualitatively analyzes the results with LIWC and keyness information . |
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. |