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The Psychological Effects of Digital Companies’ Employees during the Phase of COVID-19 Pandemic Extracted from Online Employee Reviews
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In: Sustainability; Volume 14; Issue 5; Pages: 2609 (2022)
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Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model
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In: Applied Sciences; Volume 12; Issue 2; Pages: 692 (2022)
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Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3656 (2022)
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Abstract:
A key challenge for artificial intelligence in the legal field is to determine from the text of a party’s litigation brief whether, and why, it will succeed or fail. This paper shows a proof-of-concept test case from the United States: predicting outcomes of post-grant inter partes review (IPR) proceedings for invalidating patents. The objectives are to compare decision-tree and deep learning methods, validate interpretability methods, and demonstrate outcome prediction based on party briefs. Specifically, this study compares and validates two distinct approaches: (1) representing documents with term frequency inverse document frequency (TF-IDF), training XGBoost gradient-boosted decision-tree models, and using SHAP for interpretation. (2) Deep learning of document text in context, using convolutional neural networks (CNN) with attention, and comparing LIME and attention visualization for interpretability. The methods are validated on the task of automatically determining case outcomes from unstructured written decision opinions, and then used to predict trial institution or denial based on the patent owner’s preliminary response brief. The results show how interpretable deep learning architecture classifies successful/unsuccessful response briefs on temporally separated training and test sets. More accurate prediction remains challenging, likely due to the fact-specific, technical nature of patent cases and changes in applicable law and jurisprudence over time.
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Keyword:
explainable artificial intelligence; interpretable machine learning; law; litigation; natural language processing; patents; post-grant reviews
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URL: https://doi.org/10.3390/app12073656
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Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3634 (2022)
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[Ressenya del llibre] Julià-Muné, Joan (2019): Un segle de lingüística catalana: de la Lletra de convit a la GCC (1901- 2002). Lleida: Edicions de la Universitat de Lleida, 256 p
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In: Estudis romànics, 2022, vol. 44, p. 493-495 ; Ressenyes publicades (D-FLC) (2022)
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Adaptive Kompetenzen von Kindern mit Down-Syndrom – ein Follow-up über zehn Jahre ... : Adaptive competences of children with Down syndrome - a ten-year follow-up ...
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Predicting emotional links between genre, plot, and reader response ...
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Adaptive Kompetenzen von Kindern mit Down-Syndrom – ein Follow-up über zehn Jahre ; Adaptive competences of children with Down syndrome - a ten-year follow-up
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In: Empirische Sonderpädagogik 13 (2021) 2, S. 100-109 (2021)
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School-based language and literacy interventions for multilingual children and adolescents: A systematic evidence map and an overview of reviews ...
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Sentiment analysis in Galaxy with IMDB movie review dataset ...
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Sentiment analysis in Galaxy with IMDB movie review dataset ...
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E se a marca me responde com emoji?: impacto das características da CMC na perceção e atitudes face à marca
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