Search Results for author: Marko Tadić

Found 14 papers, 0 papers with code

Multilingual Comparative Analysis of Deep-Learning Dependency Parsing Results Using Parallel Corpora

no code implementations LREC (BUCC) 2022 Diego Alves, Marko Tadić, Božo Bekavac

This article presents a comparative analysis of dependency parsing results for a set of 16 languages, coming from a large variety of linguistic families and genera, whose parallel corpora were used to train a deep-learning tool.

Dependency Parsing Language Modelling

M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets

no code implementations2 Apr 2024 Gaurish Thakkar, Sherzod Hakimov, Marko Tadić

In recent years, multimodal natural language processing, aimed at learning from diverse data types, has garnered significant attention.

Language Modelling Large Language Model +1

CroSentiNews 2.0: A Sentence-Level News Sentiment Corpus

no code implementations14 May 2023 Gaurish Thakkar, Nives Mikelic Preradović, Marko Tadić

This article presents a sentence-level sentiment dataset for the Croatian news domain.

Sentence

Croatian Film Review Dataset (Cro-FiReDa): A Sentiment Annotated Dataset of Film Reviews

no code implementations14 May 2023 Gaurish Thakkar, Nives Mikelic Preradovic, Marko Tadić

This paper introduces Cro-FiReDa, a sentiment- annotated dataset for Croatian in the domain of movie reviews.

Sentence

Building and Evaluating Universal Named-Entity Recognition English corpus

no code implementations14 Dec 2022 Diego Alves, Gaurish Thakkar, Marko Tadić

This article presents the application of the Universal Named Entity framework to generate automatically annotated corpora.

named-entity-recognition Named Entity Recognition +1

Natural Language Processing Chains Inside a Cross-lingual Event-Centric Knowledge Pipeline for European Union Under-resourced Languages

no code implementations LREC 2020 Diego Alves, Gaurish Thakkar, Marko Tadić

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.

named-entity-recognition Named Entity Recognition +1

Evaluating Language Tools for Fifteen EU-official Under-resourced Languages

no code implementations LREC 2020 Diego Alves, Gaurish Thakkar, Marko Tadić

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.

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