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  • × theme_ss:"Computerlinguistik"
  • × type_ss:"a"
  • × year_i:[2020 TO 2030}
  1. Shree, P.: ¬The journey of Open AI GPT models (2020) 0.00 
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    Abstract
    Generative Pre-trained Transformer (GPT) models by OpenAI have taken natural language processing (NLP) community by storm by introducing very powerful language models. These models can perform various NLP tasks like question answering, textual entailment, text summarisation etc. without any supervised training. These language models need very few to no examples to understand the tasks and perform equivalent or even better than the state-of-the-art models trained in supervised fashion. In this article we will cover the journey of these models and understand how they have evolved over a period of 2 years. 1. Discussion of GPT-1 paper (Improving Language Understanding by Generative Pre-training). 2. Discussion of GPT-2 paper (Language Models are unsupervised multitask learners) and its subsequent improvements over GPT-1. 3. Discussion of GPT-3 paper (Language models are few shot learners) and the improvements which have made it one of the most powerful models NLP has seen till date. This article assumes familiarity with the basics of NLP terminologies and transformer architecture.
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  2. Kurz, C.: Womit sich Strafverfolger bald befassen müssen : ChatGPT (2023) 0.00 
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  3. Siegel, M.: Maschinelle Übersetzung (2023) 0.00 
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  4. Bischoff, M.: Wie eine KI lernt, sich selbst zu erklären (2023) 0.00 
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  5. Bischoff, M.: Was steckt hinter ChatGTP & Co? (2023) 0.00 
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  6. Xiang, R.; Chersoni, E.; Lu, Q.; Huang, C.-R.; Li, W.; Long, Y.: Lexical data augmentation for sentiment analysis (2021) 0.00 
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    Abstract
    Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part-of-speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic-based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution-based data augmentation methods, one of which is thesaurus-based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST-2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM-AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well-designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms.
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  7. Hahn, U.: Automatische Sprachverarbeitung (2023) 0.00 
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  8. Schönbächler, E.; Strasser, T.; Himpsl-Gutermann, K.: Vom Chat zum Check : Informationskompetenz mit ChatGPT steigern (2023) 0.00 
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  9. Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023) 0.00 
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  10. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.00 
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  11. Szöke, D.: ChatGPT : wie Sie die KI ausprobieren können (2022) 0.00 
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  12. Albrecht, I.: GPT-3: die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität? (2023) 0.00 
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  13. Geißler, S.: Natürliche Sprachverarbeitung und Künstliche Intelligenz : ein wachsender Markt mit vielen Chancen. Das Beispiel Kairntech (2020) 0.00 
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  14. Schaer, P.: Sprachmodelle und neuronale Netze im Information Retrieval (2023) 0.00 
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  15. Räwel, J.: Automatisierte Kommunikation (2023) 0.00 
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  16. Zaitseva, E.M.: Developing linguistic tools of thematic search in library information systems (2023) 0.00 
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  17. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2022) 0.00 
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