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StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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Machine Learning approaches for Topic and Sentiment Analysis in multilingual opinions and low-resource languages: From English to Guarani
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Brazilian Portuguese verbal databases ; Bases lexicais verbais do português brasileiro
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In: Domínios de Lingu@gem; Ahead of Print ; 1980-5799 (2022)
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Representation learning of natural language and its application to language understanding and generation
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Detecting weak and strong Islamophobic hate speech on social media
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An Empirical Study of Factors Affecting Language-Independent Models
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Using Geolocated Text to Quantify Location in Real Estate Appraisal
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TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing
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Modeling Phishing Decision using Instance Based Learning and Natural Language Processing
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What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
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Abstract:
Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In this paper, we present an approach to predict the time to fix, the risk and the complexity of debugging and resolution of a bug report using different supervised machine learning algorithms namely Random Forest, Naive Bayes, SVM, MLP and XGBoost. Further, we investigate the effect of the application of active learning and we evaluate the impact of different text representation techniques, namely TF-IDF, Word2Vec, Universal Sentence Encoder and XLNet on the model's performance. The evaluation shows that a combination of text embeddings generated through the Universal Sentence Encoder and MLP as classifier outperforms all other methods, and is well suited to predict the risk and complexity of bug tickets.
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Keyword:
artificial intelligence; bug triaging; hardware development; machine learning; natural language processing; Text Analytics
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URL: http://hdl.handle.net/10125/79429 https://doi.org/10.24251/HICSS.2022.099
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Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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Um método adaptativo para análise sintática do Português Brasileiro. ; An adaptive method for syntactic analysis of Brazilian Portuguese.
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Padovani, Djalma. - : Biblioteca Digital de Teses e Dissertações da USP, 2022. : Universidade de São Paulo, 2022. : Escola Politécnica, 2022
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Multitask Pointer Network for Multi-Representational Parsing
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