1.

Record Nr.

UNINA9910703727503321

Autore

Hereford Richard

Titolo

Preliminary geologic map of the Little Colorado River Valley between Cameron and Winslow, Arizona / / by Richard Hereford

Pubbl/distr/stampa

[Reston, Va.] : , : Department of the Interior, U.S. Geological Survey, , 1979

Descrizione fisica

1 online resource (1 map)

Collana

Open-file report ; ; 79-1574

Soggetti

Geology - Little Colorado River Valley (N.M. and Ariz.)

Maps.

Little Colorado River Valley (N.M. and Ariz.) Maps

Lingua di pubblicazione

Inglese

Formato

Materiale cartografico a stampa

Livello bibliografico

Monografia

Note generali

Title from title screen (viewed on May 22, 2015).

Includes text, location diagram and township diagram.

Nota di bibliografia

Includes bibliographical references.



2.

Record Nr.

UNINA9910298447503321

Autore

Cambria Erik

Titolo

Sentic Computing : A Common-Sense-Based Framework for Concept-Level Sentiment Analysis / / by Erik Cambria, Amir Hussain

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015

ISBN

3-319-23654-7

Edizione

[1st ed. 2015.]

Descrizione fisica

1 online resource (196 p.)

Collana

Socio-Affective Computing, , 2509-5706 ; ; 1

Disciplina

610

Soggetti

Neurosciences

Data mining

Semantics

Cognitive psychology

Data Mining and Knowledge Discovery

Cognitive Psychology

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Introduction -- SenticNet -- Sentic Patterns -- Sentic Applications -- Conclusion -- Index.

Sommario/riassunto

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



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.