Nives Mikelić Preradović

Also published as: Nives Mikelic Preradovic, Nives Mikelic Preradovic, Nives Mikelić Preradović


2024

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Revealing Public Opinion Sentiment Landscape: Eurovision Song Contest Sentiment Analysis
Klara Kozolic | Gaurish Thakkar | Nives Mikelic Preradovic
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2

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FZZG at WILDRE-7: Fine-tuning Pre-trained Models for Code-mixed, Less-resourced Sentiment Analysis
Gaurish Thakkar | Marko Tadić | Nives Mikelic Preradovic
Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation

This paper describes our system used for a shared task on code-mixed, less-resourced sentiment analysis for Indo-Aryan languages. We are using the large language models (LLMs) since they have demonstrated excellent performance on classification tasks. In our participation in all tracks, we use unsloth/mistral-7b-bnb-4bit LLM for the task of code-mixed sentiment analysis. For track 1, we used a simple fine-tuning strategy on PLMs by combining data from multiple phases. Our trained systems secured first place in four phases out of five. In addition, we present the results achieved using several PLMs for each language.

2023

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Croatian Film Review Dataset (Cro-FiReDa): A Sentiment Annotated Dataset of Film Reviews
Gaurish Thakkar | Nives Mikelic Preradovic | Marko Tadić
Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)

This paper introduces Cro-FiReDa, a sentiment-annotated dataset for Croatian in the domain of movie reviews. The dataset, which contains over 10,000 sentences, has been annotated at the sentence level. In addition to presentingthe overall annotation process, we also present benchmark results based on the transformer-based fine-tuning approach.

2015

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Predicting Inflectional Paradigms and Lemmata of Unknown Words for Semi-automatic Expansion of Morphological Lexicons
Nikola Ljubešić | Miquel Esplà-Gomis | Filip Klubička | Nives Mikelić Preradović
Proceedings of the International Conference Recent Advances in Natural Language Processing

2012

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Generation of Verbal Stems in Derivationally Rich Language
Krešimir Šojat | Nives Mikelić Preradović | Marko Tadić
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The paper presents a procedure for generating prefixed verbs in Croatian comprising combinations of one, two or three prefixes. The result of this generation process is a pool of derivationally valid prefixed verbs, although not necessarily occuring in corpora. The statistics of occurences of generated verbs in Croatian National Corpus has been calculated. Further usage of such language resource with generated potential verbs is also suggested, namely, enrichment of Croatian Morphological Lexicon, Croatian Wordnet and CROVALLEX.