| 书目名称 | Understanding Editorial Text: A Computer Model of Argument Comprehension | | 编辑 | Sergio J. Alvarado | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | by Michael G. Dyer Natural language processing (NLP) is an area of research within Artificial Intelligence (AI) concerned with the comprehension and generation of natural language text. Comprehension involves the dynamic construction of conceptual representations, linked by causal relationships and organized/indexed for subsequent retrieval. Once these conceptual representations have been created, comprehension can be tested by means of such tasks as paraphrasing, question answering, and summarization. Higher-level cognitive tasks are also modeled within the NLP paradigm and include: translation, acquisition of word meanings and concepts through reading, analysis of goals and plans in multi-agent environments (e. g. , coalition and counterplanning behavior by narrative characters), invention of novel stories, recognition of abstract themes (such as irony and hypocrisy), extraction of the moral or point of a story, and justification/refutation of beliefs through argumentation. The robustness of conceptually-based text comprehension systems is directly related to the nature and scope of the knowledge constructs applied during conceptual analysis of the text. Until recently, conceptua | | 出版日期 | Book 1990 | | 关键词 | argumentation; artificial intelligence; behavior; cognition; emotion; intelligence; knowledge; knowledge en | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4613-1561-2 | | isbn_softcover | 978-1-4612-8836-7 | | isbn_ebook | 978-1-4613-1561-2Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Kluwer Academic Publishers 1990 |
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发表于 2025-3-21 16:35:39
