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Found 58 papers, 2 papers with code
RED v2: Enhancing RED Dataset for Multi-Label Emotion Detection
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.
RED: A Novel Dataset for Romanian Emotion Detection from Tweets
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.
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism
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.
Towards an Etymological Map of Romanian
In this paper we investigate the etymology of Romanian words.
Tracking Semantic Change in Cognate Sets for English and Romance Languages
To this end, we introduce a new curated dataset of cognates in all pairs of those languages.
Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange
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.
A Computational Exploration of Pejorative Language in Social Media
In this paper we study pejorative language, an under-explored topic in computational linguistics.
Automatic Discrimination between Inherited and Borrowed Latin Words in Romance Languages
In this paper, we address the problem of automatically discriminating between inherited and borrowed Latin words.
It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers
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.
An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder
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
In this work, we explore the relationship between depression and manifestations of happiness in social media.
Sequence-to-Sequence Lexical Normalization with Multilingual Transformers
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.
A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media
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
Early risk detection of mental illnesses has a massive positive impact upon the well-being of people.
An Exploratory Analysis of the Relation Between Offensive Language and Mental Health
In this paper, we analyze the interplay between the use of offensive language and mental health.
Analyzing Stylistic Variation across Different Political Regimes
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
In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages.
Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores
Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology.
Automatically Building a Multilingual Lexicon of False Friends With No Supervision
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.
Automatic Reconstruction of Missing Romanian Cognates and Unattested Latin Words
Producing related words is a key concern in historical linguistics.
Automatic Identification and Production of Related Words for Historical Linguistics
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
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
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).
From Image to Text in Sentiment Analysis via Regression and Deep Learning
In this paper, we investigate an approach for mapping images to text for three types of sentiment categories: positive, neutral and negative.
Studying Laws of Semantic Divergence across Languages using Cognate Sets
Semantic divergence in related languages is a key concern of historical linguistics.
Content Extraction and Lexical Analysis from Customer-Agent Interactions
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.
Exploring Optimism and Pessimism in Twitter Using Deep Learning
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
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.
Ab Initio: Automatic Latin Proto-word Reconstruction
Proto-word reconstruction is central to the study of language evolution.
Simulating Language Evolution: a Tool for Historical Linguistics
Language change across space and time is one of the main concerns in historical linguistics.
German Dialect Identification Using Classifier Ensembles
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.
Discriminating between Indo-Aryan Languages Using SVM Ensembles
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.
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes
Semantic difference detection attempts to capture whether a word is a discriminative attribute between two other words.
Exploring the Use of Text Classification in the Legal Domain
In this paper, we investigate the application of text classification methods to support law professionals.
On the stylistic evolution from communism to democracy: Solomon Marcus study case
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.
Finding a Character's Voice: Stylome Classification on Literary Characters
We investigate in this paper the problem of classifying the stylome of characters in a literary work.
Native Language Identification on Text and Speech
This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI).
Including Dialects and Language Varieties in Author Profiling
This paper presents a computational approach to author profiling taking gender and language variety into account.
Exploring Neural Text Simplification Models
Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction.
Ranked #14 on
Text Simplification
on TurkCorpus
Vanilla Classifiers for Distinguishing between Similar Languages
In this paper we describe the submission of the UniBuc-NLP team for the Discriminating between Similar Languages Shared Task, DSL 2016.
A Computational Perspective on the Romanian Dialects
In this paper we conduct an initial study on the dialects of Romanian.
Using Word Embeddings to Translate Named Entities
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
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
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.
