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Isabella Gagliardi and Maria Teresa Artese
When integrating data from different sources, there are problems of synonymy, different languages, and concepts of different granularity. This paper proposes a simple yet effective approach to evaluate the semantic similarity of short texts, especially k...
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Dmitry Ponkin
Pág. 18 - 29
The article studies the concept and technologies of pre-trained language models in the context of knowledge engineering. The author substantiates the relevance of the issue of the existence of internalized and implicit knowledge, extracted from text corp...
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Wenbo Zhang, Xiao Li, Yating Yang, Rui Dong and Gongxu Luo
Recently, the pretraining of models has been successfully applied to unsupervised and semi-supervised neural machine translation. A cross-lingual language model uses a pretrained masked language model to initialize the encoder and decoder of the translat...
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Tharindu Ranasinghe and Marcos Zampieri
The pervasiveness of offensive content in social media has become an important reason for concern for online platforms. With the aim of improving online safety, a large number of studies applying computational models to identify such content have been pu...
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Célia Tavares, Luciana Oliveira, Pedro Duarte and Manuel Moreira da Silva
According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as generative pretrained transformers (GPTs), may have potential implications for the job m...
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Fetoun Mansour AlZahrani and Maha Al-Yahya
Authorship attribution (AA) is a field of natural language processing that aims to attribute text to its author. Although the literature includes several studies on Arabic AA in general, applying AA to classical Arabic texts has not gained similar attent...
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Deptii Chaudhari and Ambika Vishal Pawar
Misinformation, fake news, and various propaganda techniques are increasingly used in digital media. It becomes challenging to uncover propaganda as it works with the systematic goal of influencing other individuals for the determined ends. While signifi...
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Huaqing Cheng, Shengquan Liu, Weiwei Sun and Qi Sun
Topic models can extract consistent themes from large corpora for research purposes. In recent years, the combination of pretrained language models and neural topic models has gained attention among scholars. However, this approach has some drawbacks: in...
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Fernando Fernández-Martínez, Cristina Luna-Jiménez, Ricardo Kleinlein, David Griol, Zoraida Callejas and Juan Manuel Montero
Intent recognition is a key component of any task-oriented conversational system. The intent recognizer can be used first to classify the user?s utterance into one of several predefined classes (intents) that help to understand the user?s current goal. T...
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Wael H. Gomaa, Abdelrahman E. Nagib, Mostafa M. Saeed, Abdulmohsen Algarni and Emad Nabil
Automated scoring systems have been revolutionized by natural language processing, enabling the evaluation of students? diverse answers across various academic disciplines. However, this presents a challenge as students? responses may vary significantly ...
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Shiqian Guo, Yansun Huang, Baohua Huang, Linda Yang and Cong Zhou
This paper proposed a method for improving the XLNet model to address the shortcomings of segmentation algorithm for processing Chinese language, such as long sub-word lengths, long word lists and incomplete word list coverage. To address these issues, w...
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David Kartchner, Davi Nakajima An, Wendi Ren, Chao Zhang and Cassie S. Mitchell
A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this ...
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Leon Kopitar, Iztok Fister, Jr. and Gregor Stiglic
Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to im...
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Kirill Tyshchuk, Polina Karpikova, Andrew Spiridonov, Anastasiia Prutianova, Anton Razzhigaev and Alexander Panchenko
Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based ...
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Zhipeng Zhang, Shengquan Liu and Jianming Cheng
In recent years, large-scale pretrained language models have become widely used in natural language processing tasks. On this basis, prompt learning has achieved excellent performance in specific few-shot classification scenarios. The core idea of prompt...
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Xuyang Wang, Yajun Du, Danroujing Chen, Xianyong Li, Xiaoliang Chen, Yongquan Fan, Chunzhi Xie, Yanli Li and Jia Liu
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previ...
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Konlakorn Wongpatikaseree, Sattaya Singkul, Narit Hnoohom and Sumeth Yuenyong
Language resources are the main factor in speech-emotion-recognition (SER)-based deep learning models. Thai is a low-resource language that has a smaller data size than high-resource languages such as German. This paper describes the framework of using a...
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Jawaher Alghamdi, Yuqing Lin and Suhuai Luo
Efforts have been dedicated by researchers in the field of natural language processing (NLP) to detecting and combating fake news using an assortment of machine learning (ML) and deep learning (DL) techniques. In this paper, a review of the existing stud...
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