Search Results for author: Liviu P. Dinu

Found 58 papers, 2 papers with code

RED v2: Enhancing RED Dataset for Multi-Label Emotion Detection

no code implementations LREC 2022 Alexandra Ciobotaru, Mihai Vlad Constantinescu, Liviu P. Dinu, Stefan Dumitrescu

RED (Romanian Emotion Dataset) is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts, containing single-label annotated tweets with one of the following emotions: joy, fear, sadness, anger and neutral.

Multi-Label Classification regression

RED: A Novel Dataset for Romanian Emotion Detection from Tweets

no code implementations RANLP 2021 Alexandra Ciobotaru, Liviu P. Dinu

In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts.

BIG-bench Machine Learning Opinion Mining +3

Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism

no code implementations LREC 2022 Ștefan Cobeli, Ioan-Bogdan Iordache, Shweta Yadav, Cornelia Caragea, Liviu P. Dinu, Dragoș Iliescu

Later, we devised a multi-task knowledge distillation framework to simultaneously learn the target task of optimism detection with the help of the auxiliary task of sentiment analysis and hate speech detection.

Hate Speech Detection Knowledge Distillation +1

Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange

no code implementations LREC 2022 Ioan-Bogdan Iordache, Ana Sabina Uban, Catalin Stoean, Liviu P. Dinu

It is encouraging that all models, be that they are applied to Romanian or English texts, indicate a correlation between the sentiment scores and the increase or decrease of the stock closing prices.


It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers

1 code implementation13 Jan 2023 Ana-Maria Bucur, Adrian Cosma, Paolo Rosso, Liviu P. Dinu

In this work, we propose a flexible time-enriched multimodal transformer architecture for detecting depression from social media posts, using pretrained models for extracting image and text embeddings.

Depression Detection

An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder

no code implementations2 Jul 2022 Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu, Paolo Rosso

This work proposes a transformer architecture for user-level classification of gambling addiction and depression that is trainable end-to-end.

Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression

no code implementations LREC 2022 Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu

In this work, we explore the relationship between depression and manifestations of happiness in social media.

Sequence-to-Sequence Lexical Normalization with Multilingual Transformers

no code implementations WNUT (ACL) 2021 Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu

Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text.

Lexical Normalization Machine Translation +1

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

no code implementations RANLP 2021 Ana-Maria Bucur, Ioana R. Podină, Liviu P. Dinu

In this work, we provide an extensive part-of-speech analysis of the discourse of social media users with depression.

Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT

no code implementations30 Jun 2021 Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu

Early risk detection of mental illnesses has a massive positive impact upon the well-being of people.

Analyzing Stylistic Variation across Different Political Regimes

no code implementations2 Dec 2020 Liviu P. Dinu, Ana-Sabina Uban

We also perform an analysis of the variation in topic between the two epochs, to compare with the variation at the style level.


A Computational Approach to Measuring the Semantic Divergence of Cognates

no code implementations2 Dec 2020 Ana-Sabina Uban, Alina-Maria Ciobanu, Liviu P. Dinu

In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages.

Cross-Lingual Word Embeddings Semantic Similarity +3

Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores

no code implementations3 Nov 2020 Ana-Maria Bucur, Liviu P. Dinu

Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology.

Depression Detection

Automatically Building a Multilingual Lexicon of False Friends With No Supervision

no code implementations LREC 2020 Ana Sabina Uban, Liviu P. Dinu

Cognate words, defined as words in different languages which derive from a common etymon, can be useful for language learners, who can leverage the orthographical similarity of cognates to more easily understand a text in a foreign language.

Cross-Lingual Word Embeddings Language Acquisition +1

Automatic Identification and Production of Related Words for Historical Linguistics

no code implementations CL 2019 Alina Maria Ciobanu, Liviu P. Dinu

We apply our method to multiple data sets, showing that our approach improves on previous results, also having the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.

The Myth of Double-Blind Review Revisited: ACL vs. EMNLP

no code implementations IJCNLP 2019 Cornelia Caragea, Ana Uban, Liviu P. Dinu

We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper.

Linguistic classification: dealing jointly with irrelevance and inconsistency

no code implementations RANLP 2019 Laura Franzoi, Andrea Sgarro, Anca Dinu, Liviu P. Dinu

In this paper, we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i. e. features which should not contribute to the classification) and even inconsistent features (which do not make sense for specific languages).

Classification General Classification +1

From Image to Text in Sentiment Analysis via Regression and Deep Learning

no code implementations RANLP 2019 Daniela Onita, Liviu P. Dinu, Adriana Birlutiu

In this paper, we investigate an approach for mapping images to text for three types of sentiment categories: positive, neutral and negative.

regression Sentiment Analysis

Content Extraction and Lexical Analysis from Customer-Agent Interactions

no code implementations WS 2018 Sergiu Nisioi, Anca Bucur, Liviu P. Dinu

In this paper, we provide a lexical comparative analysis of the vocabulary used by customers and agents in an Enterprise Resource Planning (ERP) environment and a potential solution to clean the data and extract relevant content for NLP.

Lexical Analysis

Exploring Optimism and Pessimism in Twitter Using Deep Learning

no code implementations EMNLP 2018 Cornelia Caragea, Liviu P. Dinu, Bogdan Dumitru

Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas.

Classifier Ensembles for Dialect and Language Variety Identification

no code implementations14 Aug 2018 Liviu P. Dinu, Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi

In this paper we present ensemble-based systems for dialect and language variety identification using the datasets made available by the organizers of the VarDial Evaluation Campaign 2018.

Dialect Identification

German Dialect Identification Using Classifier Ensembles

no code implementations COLING 2018 Alina Maria Ciobanu, Shervin Malmasi, Liviu P. Dinu

In this paper we present the GDI_classification entry to the second German Dialect Identification (GDI) shared task organized within the scope of the VarDial Evaluation Campaign 2018.

Dialect Identification

Discriminating between Indo-Aryan Languages Using SVM Ensembles

no code implementations COLING 2018 Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Santanu Pal, Liviu P. Dinu

In this paper we present a system based on SVM ensembles trained on characters and words to discriminate between five similar languages of the Indo-Aryan family: Hindi, Braj Bhasha, Awadhi, Bhojpuri, and Magahi.

Language Identification

On the stylistic evolution from communism to democracy: Solomon Marcus study case

no code implementations RANLP 2017 Anca Dinu, Liviu P. Dinu, Bogdan Dumitru

In this article we propose a stylistic analysis of Solomon Marcus{'} non-scientific published texts, gathered in six volumes, aiming to uncover some of his quantitative and qualitative fingerprints.

Native Language Identification on Text and Speech

no code implementations WS 2017 Marcos Zampieri, Alina Maria Ciobanu, Liviu P. Dinu

This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI).

Native Language Identification

Including Dialects and Language Varieties in Author Profiling

no code implementations3 Jul 2017 Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Liviu P. Dinu

This paper presents a computational approach to author profiling taking gender and language variety into account.

Exploring Neural Text Simplification Models

1 code implementation ACL 2017 Sergiu Nisioi, Sanja {\v{S}}tajner, Simone Paolo Ponzetto, Liviu P. Dinu

Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction.

Lexical Simplification Machine Translation +2

Vanilla Classifiers for Distinguishing between Similar Languages

no code implementations WS 2016 Sergiu Nisioi, Alina Maria Ciobanu, Liviu P. Dinu

In this paper we describe the submission of the UniBuc-NLP team for the Discriminating between Similar Languages Shared Task, DSL 2016.

Information Retrieval Language Identification +3

Using Word Embeddings to Translate Named Entities

no code implementations LREC 2016 Octavia-Maria {\c{S}}ulea, Sergiu Nisioi, Liviu P. Dinu

In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introducing a novel, yet simple way of obtaining bilingual word vectors.

Chinese Named Entity Recognition named-entity-recognition +4

A Corpus of Native, Non-native and Translated Texts

no code implementations LREC 2016 Sergiu Nisioi, Ella Rabinovich, Liviu P. Dinu, Shuly Wintner

We describe a monolingual English corpus of original and (human) translated texts, with an accurate annotation of speaker properties, including the original language of the utterances and the speaker{'}s country of origin.

The Romanian Neuter Examined Through A Two-Gender N-Gram Classification System

no code implementations LREC 2012 Liviu P. Dinu, Vlad Niculae, Octavia-Maria {\c{S}}ulea

A recent analysis of the Romanian gender system described in (Bateman and Polinsky, 2010), based on older observations, argues that there are two lexically unspecified noun classes in the singular and two different ones in the plural and that what is generally called neuter in Romanian shares the class in the singular with masculines, and the class in the plural with feminines based not only on agreement features but also on form.

General Classification

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