Is a Pretrained Model the Answer to Situational Awareness Detection on Social Media? [PDF]
, 2023 Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important ...
Lee, Kahhe, Lo, Siaw Ling, Zhang, Yuhao
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An Investigation of Structures Responsible for Gender Bias in BERT and DistilBERT
, 2023 In recent years, large Transformer-based Pre-trained Language Models (PLM) have changed the Natural Language Processing (NLP) landscape, by pushing the performance boundaries of the state-of-the-art on a wide variety of tasks. However, this performance gain goes along with an increase in complexity, and as a result, the size of such models (up to ...
Thibaud Leteno +3 more
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A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT
IEEE Access, 2023 While user-oriented service industries are rapidly growing, various network devices provide these services through different access paths. Accordingly, the network flow is also increasing explosively. As demand for management related to limited network resources increases, the network traffic classification grows to prominence.
Chang-Yui Shin +3 more
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Analysing Natural Language Processing Techniques: A Comparative Study of NLTK, spaCy, BERT, and DistilBERT on Customer Query Datasets [PDF]
, 2022 Sentiment analysis within customer queries stems from its critical role in shaping the perception of a company’s brand. Poor handling of customer queries may lead to adverse consequences.
De Camillis, Patrizia
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A PERFORMANCE ANALYSIS OF SUGGESTION MINING IN MOBILE APP REVIEWS USING LARGE LANGUAGE MODELS TRANSFER LEARNING TECHNIQUES [PDF]
Proceedings on Engineering SciencesOne of the enduring issues with obtaining user feedback is to find effective ways of actionable improvement recommendations from this feedback for quality mobile app development.
Makarand Lotan Mali , Nitin N. Patil
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Authorship identification using ensemble learning
Scientific Reports, 2022 With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing.
Ahmed Abbasi +5 more
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Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
, 2020 Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice.
Frieder, Ophir +5 more
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Multilingual Fine-Grained Named Entity Recognition [PDF]
Computer Science Journal of Moldova, 2023 The “MultiCoNER II Multilingual Complex Named Entity Recognition” task\footnote[1]{\url{https://multiconer.github.io}} within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs,
Viorica-Camelia Lupancu, Adrian Iftene
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Data Augmentation Methods for Enhancing Robustness in Text Classification Tasks
Algorithms, 2023 Text classification is widely studied in natural language processing (NLP). Deep learning models, including large pre-trained models like BERT and DistilBERT, have achieved impressive results in text classification tasks.
Huidong Tang +2 more
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Evaluating the Performance Impact of Fine-Tuning Optimization Strategies on Pre-Trained DistilBERT Models Towards Hate Speech Detection in Social Media [PDF]
, 2022 Hate speech can be defined as forms of expression that incite hatred or encourage violence towards a person or group based on race, religion, gender, or sexual orientation.
McGovern, Aidan
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