Search Results for author:
Found 13 papers, 2 papers with code
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets
In recent years, multimodal natural language processing, aimed at learning from diverse data types, has garnered significant attention.
CroSentiNews 2.0: A Sentence-Level News Sentiment Corpus
This article presents a sentence-level sentiment dataset for the Croatian news domain.
Croatian Film Review Dataset (Cro-FiReDa): A Sentiment Annotated Dataset of Film Reviews
This paper introduces Cro-FiReDa, a sentiment- annotated dataset for Croatian in the domain of movie reviews.
OEKG: The Open Event Knowledge Graph
Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders.
Building Multilingual Corpora for a Complex Named Entity Recognition and Classification Hierarchy using Wikipedia and DBpedia
With the ever-growing popularity of the field of NLP, the demand for datasets in low resourced-languages follows suit.
Quotations, Coreference Resolution, and Sentiment Annotations in Croatian News Articles: An Exploratory Study
This paper presents a corpus annotated for the task of direct-speech extraction in Croatian.
Building and Evaluating Universal Named-Entity Recognition English corpus
This article presents the application of the Universal Named Entity framework to generate automatically annotated corpora.
Multi-task Learning for Cross-Lingual Sentiment Analysis
This paper presents a cross-lingual sentiment analysis of news articles using zero-shot and few-shot learning.
Natural Language Processing Chains Inside a Cross-lingual Event-Centric Knowledge Pipeline for European Union Under-resourced Languages
Due to the differences in terms of availability of language resources for each language, we have built this strategy in three steps, starting with processing chains for the well-resourced languages and finishing with the development of new modules for the under-resourced ones.
Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets
In this paper, we present various pre-training strategies that aid in im-proving the accuracy of the sentiment classification task.
Evaluating Language Tools for Fifteen EU-official Under-resourced Languages
We considered the difference between reported and our tested results within a single percentage point as being within the limits of acceptable tolerance and thus consider this result as reproducible.
UNER: Universal Named-Entity RecognitionFramework
We introduce the Universal Named-Entity Recognition (UNER)framework, a 4-level classification hierarchy, and the methodology that isbeing adopted to create the first multilingual UNER corpus: the SETimesparallel corpus annotated for named-entities.
