語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Cognitively inspired natural languag...
~
Mishra, Abhijit.
FindBook
Google Book
Amazon
博客來
Cognitively inspired natural language processing = an investigation based on eye-tracking /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Cognitively inspired natural language processing/ by Abhijit Mishra, Pushpak Bhattacharyya.
其他題名:
an investigation based on eye-tracking /
作者:
Mishra, Abhijit.
其他作者:
Bhattacharyya, Pushpak.
出版者:
Singapore :Springer Singapore : : 2018.,
面頁冊數:
xvii, 174 p. :ill. (some col.), digital ;24 cm.
內容註:
Chapter 1. Introduction -- Chapter 2. Eye-tracking: Theory, Methods, and Applications in Language Processing and Other Areas -- Chapter 3. Estimating Annotation Complexities of Text Using Gaze and Textual Information - Case studies of Translation and Sentiment Annotation -- Chapter 4. Scanpath Complexity: Combining Gaze Attributes for Modeling Effort in Text Reading/Annotation -- Chapter 5. Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior -- Chapter 6. Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detection -- Chapter 7. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network -- Chapter 8. Conclusion and Future Directions.
Contained By:
Springer eBooks
標題:
Natural language processing (Computer science) -
電子資源:
http://dx.doi.org/10.1007/978-981-13-1516-9
ISBN:
9789811315169
Cognitively inspired natural language processing = an investigation based on eye-tracking /
Mishra, Abhijit.
Cognitively inspired natural language processing
an investigation based on eye-tracking /[electronic resource] :by Abhijit Mishra, Pushpak Bhattacharyya. - Singapore :Springer Singapore :2018. - xvii, 174 p. :ill. (some col.), digital ;24 cm. - Cognitive intelligence and robotics,2520-1956. - Cognitive intelligence and robotics..
Chapter 1. Introduction -- Chapter 2. Eye-tracking: Theory, Methods, and Applications in Language Processing and Other Areas -- Chapter 3. Estimating Annotation Complexities of Text Using Gaze and Textual Information - Case studies of Translation and Sentiment Annotation -- Chapter 4. Scanpath Complexity: Combining Gaze Attributes for Modeling Effort in Text Reading/Annotation -- Chapter 5. Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior -- Chapter 6. Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detection -- Chapter 7. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network -- Chapter 8. Conclusion and Future Directions.
This book shows ways of augmenting the capabilities of Natural Language Processing (NLP) systems by means of cognitive-mode language processing. The authors employ eye-tracking technology to record and analyze shallow cognitive information in the form of gaze patterns of readers/annotators who perform language processing tasks. The insights gained from such measures are subsequently translated into systems that help us (1) assess the actual cognitive load in text annotation, with resulting increase in human text-annotation efficiency, and (2) extract cognitive features that, when added to traditional features, can improve the accuracy of text classifiers. In sum, the authors' work successfully demonstrates that cognitive information gleaned from human eye-movement data can benefit modern NLP. Currently available Natural Language Processing (NLP) systems are weak AI systems: they seek to capture the functionality of human language processing, without worrying about how this processing is realized in human beings' hardware. In other words, these systems are oblivious to the actual cognitive processes involved in human language processing. This ignorance, however, is NOT bliss! The accuracy figures of all non-toy NLP systems saturate beyond a certain point, making it abundantly clear that "something different should be done.".
ISBN: 9789811315169
Standard No.: 10.1007/978-981-13-1516-9doiSubjects--Topical Terms:
565309
Natural language processing (Computer science)
LC Class. No.: QA76.9.N38
Dewey Class. No.: 006.35
Cognitively inspired natural language processing = an investigation based on eye-tracking /
LDR
:03152nmm a2200325 a 4500
001
2152161
003
DE-He213
005
20180801111727.0
006
m d
007
cr nn 008maaau
008
190403s2018 si s 0 eng d
020
$a
9789811315169
$q
(electronic bk.)
020
$a
9789811315152
$q
(paper)
024
7
$a
10.1007/978-981-13-1516-9
$2
doi
035
$a
978-981-13-1516-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA76.9.N38
072
7
$a
UYQL
$2
bicssc
072
7
$a
COM042000
$2
bisacsh
082
0 4
$a
006.35
$2
23
090
$a
QA76.9.N38
$b
M678 2018
100
1
$a
Mishra, Abhijit.
$3
3338189
245
1 0
$a
Cognitively inspired natural language processing
$h
[electronic resource] :
$b
an investigation based on eye-tracking /
$c
by Abhijit Mishra, Pushpak Bhattacharyya.
260
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2018.
300
$a
xvii, 174 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
490
1
$a
Cognitive intelligence and robotics,
$x
2520-1956
505
0
$a
Chapter 1. Introduction -- Chapter 2. Eye-tracking: Theory, Methods, and Applications in Language Processing and Other Areas -- Chapter 3. Estimating Annotation Complexities of Text Using Gaze and Textual Information - Case studies of Translation and Sentiment Annotation -- Chapter 4. Scanpath Complexity: Combining Gaze Attributes for Modeling Effort in Text Reading/Annotation -- Chapter 5. Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior -- Chapter 6. Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detection -- Chapter 7. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network -- Chapter 8. Conclusion and Future Directions.
520
$a
This book shows ways of augmenting the capabilities of Natural Language Processing (NLP) systems by means of cognitive-mode language processing. The authors employ eye-tracking technology to record and analyze shallow cognitive information in the form of gaze patterns of readers/annotators who perform language processing tasks. The insights gained from such measures are subsequently translated into systems that help us (1) assess the actual cognitive load in text annotation, with resulting increase in human text-annotation efficiency, and (2) extract cognitive features that, when added to traditional features, can improve the accuracy of text classifiers. In sum, the authors' work successfully demonstrates that cognitive information gleaned from human eye-movement data can benefit modern NLP. Currently available Natural Language Processing (NLP) systems are weak AI systems: they seek to capture the functionality of human language processing, without worrying about how this processing is realized in human beings' hardware. In other words, these systems are oblivious to the actual cognitive processes involved in human language processing. This ignorance, however, is NOT bliss! The accuracy figures of all non-toy NLP systems saturate beyond a certain point, making it abundantly clear that "something different should be done.".
650
0
$a
Natural language processing (Computer science)
$3
565309
650
0
$a
Machine learning.
$3
533906
650
0
$a
Eye tracking.
$3
2118543
650
1 4
$a
Computer Science.
$3
626642
650
2 4
$a
Language Translation and Linguistics.
$3
892561
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
890894
650
2 4
$a
Computational Linguistics.
$3
893900
650
2 4
$a
Psycholinguistics.
$3
517043
700
1
$a
Bhattacharyya, Pushpak.
$3
3217984
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
830
0
$a
Cognitive intelligence and robotics.
$3
3338190
856
4 0
$u
http://dx.doi.org/10.1007/978-981-13-1516-9
950
$a
Computer Science (Springer-11645)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9352293
電子資源
11.線上閱覽_V
電子書
EB QA76.9.N38
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入