%0 e Book %A Okazaki, Naoaki and Yada, Katsutoshi and Satoh, Ken and Mineshima, Koji %E Okazaki, Naoaki %E Yada, Katsutoshi %E Satoh, Ken %E Mineshima, Koji %I Springer International Publishing %D 2021 %C Cham %I : Imprint: Springer %C ; Cham %D 2021 %G English %B Lecture Notes in Artificial Intelligence %@ 9783030799427 %~ Universitätsbibliothek "Georgius Agricola" %T New Frontiers in Artificial Intelligence: JSAI-isAI 2020 Workshops, JURISIN, LENLS 2020 Workshops, Virtual Event, November 15–17, 2020, Revised Selected Papers %U https://doi.org/10.1007/978-3-030-79942-7 %7 1st ed. 2021. %X LENLS Logic and Engineering of Natural Language Semantics (LENLS) -- A Semantics for “Typically” in First-Order Default Reasoning -- On dialogue modeling: a dynamic epistemic inquisitive approach -- Knowledge acquisition from natural language with Treebank Semantics and Flora -- The Explicated Addressee: A (mainly) pragmatic account of Japanese ka-questions -- Superlative Modifiers as Concessive Conditionals -- Polynomial Event Semantics: Negation -- Oleg Kiselyov Experiential Imagination and the Inside/Outside-Distinction -- Against the multidimensional approach to honorific meaning: A solution to the binding problem of conventional implicature -- A Persona-based Analysis of Politeness in Japanese and Spanish -- JURISIN2020 Fourteenth International Workshop on Juris-informatics (JURISIN 2020) -- AI and Judicial Policy -- Differential Translation for Partially Amended Japanese Statutory Sentences -- Aspect Classification for Legal Depositions -- COLIEE 2020: Methods for Legal Document Retrieval and Entailment -- COLIEE 2020: Legal Information Retrieval & Entailment with Legal Embeddings and Boosting -- BERT-Based Ensemble Model for Statute Law Retrieval and Legal Information Entailment -- Chuen Huang The Application of Text Entailment Techniques in COLIEE 2020 -- Information Extraction / Entailment of Common Law & Civil Code -- Paragraph Similarity Scoring and Fine-Tuned BERT for Legal Information Retrieval and Entailment -- Using BERT and TF-IDF to Predict Entailment in Law-Based Queries. %Z https://katalog.ub.tu-freiberg.de/Record/0-1761809385 %U https://katalog.ub.tu-freiberg.de/Record/0-1761809385