Understanding Editorial Text: A Computer Model of Argument Comprehension:
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1990
|
| Schriftenreihe: | The Kluwer International Series in Engineering and Computer Science, Natural Language Processing and Machine Translation
107 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | 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, conceptually-based natural language systems were developed for, and applied to, the task of narrative comprehension (Dyer, 1983a; Schank and Abelson, 1977; Wilensky, 1983). These systems worked by recognizing the goals and plans of narrative characters, and. using this knowledge to build a conceptual representation of the narrative, xx UNDERSTANDING EDITORIAL TEXT including actions and intentions which must be inferred to complete the representation. A large portion of text appearing in newspapers and magazines, however, is editorial in nature |
| Beschreibung: | 1 Online-Ressource (XXVIII, 296 p) |
| ISBN: | 9781461315612 |
| DOI: | 10.1007/978-1-4613-1561-2 |
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4613-1561-2 |
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| spelling | Alvarado, Sergio J. Verfasser aut Understanding Editorial Text: A Computer Model of Argument Comprehension by Sergio J. Alvarado Boston, MA Springer US 1990 1 Online-Ressource (XXVIII, 296 p) txt rdacontent c rdamedia cr rdacarrier The Kluwer International Series in Engineering and Computer Science, Natural Language Processing and Machine Translation 107 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, conceptually-based natural language systems were developed for, and applied to, the task of narrative comprehension (Dyer, 1983a; Schank and Abelson, 1977; Wilensky, 1983). These systems worked by recognizing the goals and plans of narrative characters, and. using this knowledge to build a conceptual representation of the narrative, xx UNDERSTANDING EDITORIAL TEXT including actions and intentions which must be inferred to complete the representation. A large portion of text appearing in newspapers and magazines, however, is editorial in nature Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Erscheint auch als Druck-Ausgabe 9781461288367 https://doi.org/10.1007/978-1-4613-1561-2 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Alvarado, Sergio J. Understanding Editorial Text: A Computer Model of Argument Comprehension Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence |
| subject_GND | (DE-588)4113937-9 |
| title | Understanding Editorial Text: A Computer Model of Argument Comprehension |
| title_auth | Understanding Editorial Text: A Computer Model of Argument Comprehension |
| title_exact_search | Understanding Editorial Text: A Computer Model of Argument Comprehension |
| title_full | Understanding Editorial Text: A Computer Model of Argument Comprehension by Sergio J. Alvarado |
| title_fullStr | Understanding Editorial Text: A Computer Model of Argument Comprehension by Sergio J. Alvarado |
| title_full_unstemmed | Understanding Editorial Text: A Computer Model of Argument Comprehension by Sergio J. Alvarado |
| title_short | Understanding Editorial Text: A Computer Model of Argument Comprehension |
| title_sort | understanding editorial text a computer model of argument comprehension |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4613-1561-2 |
| work_keys_str_mv | AT alvaradosergioj understandingeditorialtextacomputermodelofargumentcomprehension |