Search Results for author:
Found 87 papers, 21 papers with code
Do Dependency Relations Help in the Task of Stance Detection?
Furthermore, we study the phenomenon of stance with respect to six different targets – one per language, and two different for Italian – employing a variety of machine learning algorithms that primarily exploit morphological and syntactic knowledge as features, represented throughout the format of Universal Dependencies.
UPV at the Arabic Hate Speech 2022 Shared Task: Offensive Language and Hate Speech Detection using Transformers and Ensemble Models
This paper describes our participation in the shared task Fine-Grained Hate Speech Detection on Arabic Twitter at the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT).
Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection
Hate speech detection is a prominent and challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author.
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI), which explores the detection of misogynous memes on the web by taking advantage of available texts and images.
Arabic WordNet: New Content and New Applications
We also present how this content helps in the implementation of new Arabic NLP applications, especially for Question Answering (QA) systems.
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media
We propose that transfer learning with linguistic features can be useful for approaching both the technical problem of improving mental disorder detection in the context of data scarcity, and the clinical problem of understanding the overlapping symptoms between certain disorders.
Understanding Patterns of Anorexia Manifestations in Social Media Data with Deep Learning
Eating disorders are a growing problem especially among young people, yet they have been under-studied in computational research compared to other mental health disorders such as depression.
Masking and Transformer-based Models for Hyperpartisanship Detection in News
Hyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation.
RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian
Recently, large language models (LLMs) have become increasingly powerful and have become capable of solving a plethora of tasks through proper instructions in natural language.
Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues
Depression, a prominent contributor to global disability, affects a substantial portion of the population.
Toxic language detection: a systematic review of Arabic datasets
The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need.
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection
In this work, we resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.
Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains
This paper presents the overview of the AuTexTification shared task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN 2023 conference.
Mitigating Negative Transfer with Task Awareness for Sexism, Hate Speech, and Toxic Language Detection
Therefore another solution, based on the sharing of information between tasks, has been developed: Multi-Task Learning (MTL).
Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages
This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022.
Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages.
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.
Fake News and Hate Speech: Language in Common
In this paper we raise the research question of whether fake news and hate speech spreaders share common patterns in language.
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020
This overview paper describes the first shared task on fake news detection in Urdu language.
UrduFake@FIRE2020: Shared Track on Fake News Identification in Urdu
This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language.
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.
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models
Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media.
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios
Unfortunately, there is a lack of data in the conversational domain.
Unsupervised Ranking and Aggregation of Label Descriptions for Zero-Shot Classifiers
Zero-shot text classifiers based on label descriptions embed an input text and a set of labels into the same space: measures such as cosine similarity can then be used to select the most similar label description to the input text as the predicted label.
UPV at TREC Health Misinformation Track 2021 Ranking with SBERT and Quality Estimators
Health misinformation on search engines is a significant problem that could negatively affect individuals or public health.
UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims
Identifying check-worthy claims is often the first step of automated fact-checking systems.
Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language
With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets' credibility.
RoMa at SemEval-2021 Task 7: A Transformer-based Approach for Detecting and Rating Humor and Offense
The SNN model is used for learning a latent space where instances of humor and non-humor can be distinguished.
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information
To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture.
Analysis and tuning of hierarchical topic models based on Renyi entropy approach
In this paper, we propose a Renyi entropy-based approach for a partial solution to the above problem.
PRHLT-UPV at SemEval-2020 Task 12: BERT for Multilingual Offensive Language Detection
We propose a model based on the BERT architecture for the analysis of texts in English.
LIMSI\_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
This paper describes the participation of LIMSI{\_}UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.
PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis
The BERT model is used to process the textual information and VGG the images.
Multilingual Irony Detection with Dependency Syntax and Neural Models
This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian).
Classifier Combination Approach for Question Classification for Bengali Question Answering System
Overall, the stacking approach produces the best results for fine-grained classification and achieves 87. 79% of accuracy.
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.
#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
Interest has grown around the classification of stance that users assume within online debates in recent years.
NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching
Embeddings have become a key paradigm to learn graph represen-tations and facilitate downstream graph analysis tasks.
Profiling Bots, Fake News Spreaders and Haters
In 2021 we specifically aim at addressing the challenging problem of profiling haters in social media in order to monitor abusive language and prevent cases of social exclusion in order to combat, for instance, racism, xenophobia and misogyny.
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus
In this paper we describe a fine-grained annotation scheme centered on irony, in which we highlight the tokens that are responsible for its activation, (irony activators) and their morpho-syntactic features.
Irony Detection in a Multilingual Context
This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system.
FacTweet: Profiling Fake News Twitter Accounts
We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features.
TexTrolls: Identifying Russian Trolls on Twitter from a Textual Perspective
Our approach is mainly based on textual features which utilize thematic information, and profiling features to identify the accounts from their way of writing tweets.
An Emotional Analysis of False Information in Social Media and News Articles
Fake news is risky since it has been created to manipulate the readers' opinions and beliefs.
Unmasking Bias in News
We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand.
DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets
This paper describes the system we developed for SemEval 2019 on Identifying and Categorizing Offensive Language in Social Media (OffensEval - Task 6).
TUVD team at SemEval-2019 Task 6: Offense Target Identification
This article presents our approach for detecting a target of offensive messages in Twitter, including Individual, Group and Others classes.
UPV-28-UNITO at SemEval-2019 Task 7: Exploiting Post's Nesting and Syntax Information for Rumor Stance Classification
In the present paper we describe the UPV-28-UNITO system{'}s submission to the RumorEval 2019 shared task.
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter.
Stance Detection in Fake News A Combined Feature Representation
Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT.
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter
This paper describes an ensemble approach to the SemEval-2018 Task 3.
ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets
In this paper we describe the system used by the ValenTO team in the shared task on Irony Detection in English Tweets at SemEval 2018.
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection
In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection.
Cross-corpus Native Language Identification via Statistical Embedding
In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus.
Semantically-informed distance and similarity measures for paraphrase plagiarism identification
Paraphrase plagiarism identification represents a very complex task given that plagiarized texts are intentionally modified through several rewording techniques.
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity
In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages.
Cross-Lingual Information Retrieval
Cross-Lingual Semantic Textual Similarity
+9
Learning Multimodal Gender Profile using Neural Networks
Gender identification in social networks is one of the most popular aspects of user profile learning.
Sentence Alignment Methods for Improving Text Simplification Systems
We provide several methods for sentence-alignment of texts with different complexity levels.
A Low Dimensionality Representation for Language Variety Identification
We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ~35%.
Author Profiling at PAN: from Age and Gender Identification to Language Variety Identification (invited talk)
Author profiling is the study of how language is shared by people, a problem of growing importance in applications dealing with security, in order to understand who could be behind an anonymous threat message, and marketing, where companies may be interested in knowing the demographics of people that in online reviews liked or disliked their products.
Convolutional Neural Networks for Authorship Attribution of Short Texts
We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams.
Single and Cross-domain Polarity Classification using String Kernels
That setting usually implies the use of a domain adaptation method.
Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets
Stance detection, the task of identifying the speaker's opinion towards a particular target, has attracted the attention of researchers.
Enrichment of Bilingual Dictionary through News Stream Data
Many time the language pair does not have large bilingual comparable corpora and in such cases the best automatic dictionary is upper bounded by the quality and coverage of such corpora.
Squeezing bottlenecks: exploring the limits of autoencoder semantic representation capabilities
We present a comprehensive study on the use of autoencoders for modelling text data, in which (differently from previous studies) we focus our attention on the following issues: i) we explore the suitability of two different models bDA and rsDA for constructing deep autoencoders for text data at the sentence level; ii) we propose and evaluate two novel metrics for better assessing the text-reconstruction capabilities of autoencoders; and iii) we propose an automatic method to find the critical bottleneck dimensionality for text language representations (below which structural information is lost).
Evaluating the Similarity Estimator component of the TWIN Personality-based Recommender System
With the constant increase in the amount of information available in online communities, the task of building an appropriate Recommender System to support the user in her decision making process is becoming more and more challenging.
