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Cultural Evolution of Precise and Agreed‐Upon Semantic Conventions in a Multiplayer Gaming App
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In: ISSN: 0364-0213 ; EISSN: 1551-6709 ; Cognitive Science ; https://jeannicod.ccsd.cnrs.fr/ijn_03636720 ; Cognitive Science, Wiley, 2022, 46 (2), ⟨10.1111/cogs.13113⟩ (2022)
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The slíbhín and the créatúr: an examination of the lived experiences of regional or minority language users within the criminal justice System
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In: McEvoy, Géaróidín (2022) The slíbhín and the créatúr: an examination of the lived experiences of regional or minority language users within the criminal justice System. PhD thesis, Dublin City University. (2022)
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Legitimacy and legality in national identity construction ...
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ГИПЕРКОРРЕКТНОСТЬ КАК ПРИЧИНА ОШИБОК В РЕЧИ УЧАЩИХСЯ ... : HYPERCORRECTION AS A CAUSE OF ERRORS IN THE SPEECH OF STUDENTS ...
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Arguing About “COVID” ; Metalinguistic Arguments on What Counts as a “COVID-19 Death”
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Cross-currents: Indigenous language interpreting in Australia's justice system (Public Version) .pdf ...
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Cross-currents: Indigenous language interpreting in Australia's justice system (Public Version) .pdf ...
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Pre-service teachers’ attitudes toward the teaching of Mandarin in South Africa ...
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Pre-service teachers’ attitudes toward the teaching of Mandarin in South Africa ...
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| 11 |
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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| 12 |
An Information Theoretic Approach to Symbolic Learning in Synthetic Languages
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In: Entropy; Volume 24; Issue 2; Pages: 259 (2022)
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Language Provision in the Scottish Public Sector: Recommendations to Promote Inclusive Practice
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In: Social Inclusion ; 9 ; 1 ; 45-55 ; Social Inclusion and Multilingualism: The Impact of Linguistic Justice, Economy of Language and Language Policy (2022)
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