Florin Pop


2023

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From Fake to Hyperpartisan News Detection Using Domain Adaptation
Răzvan-Alexandru Smădu | Sebastian-Vasile Echim | Dumitru-Clementin Cercel | Iuliana Marin | Florin Pop
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.

2022

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Legal Named Entity Recognition with Multi-Task Domain Adaptation
Răzvan-Alexandru Smădu | Ion-Robert Dinică | Andrei-Marius Avram | Dumitru-Clementin Cercel | Florin Pop | Mihaela-Claudia Cercel
Proceedings of the Natural Legal Language Processing Workshop 2022

Named Entity Recognition (NER) is a well-explored area from Information Retrieval and Natural Language Processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1%, while considerable improvements are in the recall metric.

2017

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oIQa: An Opinion Influence Oriented Question Answering Framework with Applications to Marketing Domain
Dumitru-Clementin Cercel | Cristian Onose | Stefan Trausan-Matu | Florin Pop
Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017

Understanding questions and answers in QA system is a major challenge in the domain of natural language processing. In this paper, we present a question answering system that influences the human opinions in a conversation. The opinion words are quantified by using a lexicon-based method. We apply Latent Semantic Analysis and the cosine similarity measure between candidate answers and each question to infer the answer of the chatbot.