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1. |
Record Nr. |
UNINA9910703727503321 |
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Autore |
Hereford Richard |
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Titolo |
Preliminary geologic map of the Little Colorado River Valley between Cameron and Winslow, Arizona / / by Richard Hereford |
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Pubbl/distr/stampa |
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[Reston, Va.] : , : Department of the Interior, U.S. Geological Survey, , 1979 |
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Descrizione fisica |
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1 online resource (1 map) |
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Collana |
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Open-file report ; ; 79-1574 |
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Soggetti |
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Geology - Little Colorado River Valley (N.M. and Ariz.) |
Maps. |
Little Colorado River Valley (N.M. and Ariz.) Maps |
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Lingua di pubblicazione |
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Formato |
Materiale cartografico a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from title screen (viewed on May 22, 2015). |
Includes text, location diagram and township diagram. |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNINA9910298447503321 |
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Autore |
Cambria Erik |
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Titolo |
Sentic Computing : A Common-Sense-Based Framework for Concept-Level Sentiment Analysis / / by Erik Cambria, Amir Hussain |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (196 p.) |
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Collana |
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Socio-Affective Computing, , 2509-5706 ; ; 1 |
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Disciplina |
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Soggetti |
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Neurosciences |
Data mining |
Semantics |
Cognitive psychology |
Data Mining and Knowledge Discovery |
Cognitive Psychology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index. |
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Sommario/riassunto |
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This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web. Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed: • Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference • Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence |
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frequencies in text • Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction and systems. |
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