語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ subject:"Machine Learning." ]
切換:
標籤
|
MARC模式
|
ISBD
Machine learning for text
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Machine learning for textby Charu C. Aggarwal.
作者:
Aggarwal, Charu C.
出版者:
Cham :Springer International Publishing :2022.
面頁冊數:
xxiii, 565 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Machine learning.
電子資源:
https://doi.org/10.1007/978-3-030-96623-2
ISBN:
9783030966232$q(electronic bk.)
Machine learning for text
Aggarwal, Charu C.
Machine learning for text
[electronic resource] /by Charu C. Aggarwal. - Second edition. - Cham :Springer International Publishing :2022. - xxiii, 565 p. :ill., digital ;24 cm.
1 An Introduction to Text Analytics -- 2 Text Preparation and Similarity Computation -- 3 Matrix Factorization and Topic Modeling -- 4 Text Clustering -- 5 Text Classification: Basic Models -- 6 Linear Models for Classification and Regression -- 7 Classifier Performance and Evaluation -- 8 Joint Text Mining with Heterogeneous Data -- 9 Information Retrieval and Search Engines -- 10 Language Modeling and Deep Learning -- 11 Attention Mechanisms and Transformers -- 12 Text Summarization -- 13 Information Extraction and Knowledge Graphs -- 14 Question Answering -- 15 Opinion Mining and Sentiment Analysis -- 16 Text Segmentation and Event Detection.
This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.
ISBN: 9783030966232$q(electronic bk.)
Standard No.: 10.1007/978-3-030-96623-2doiSubjects--Topical Terms:
188639
Machine learning.
LC Class. No.: Q325.5
Dewey Class. No.: 006.31
Machine learning for text
LDR
:03262nmm a2200337 a 4500
001
646190
003
DE-He213
005
20220504084806.0
006
m d
007
cr nn 008maaau
008
231219s2022 sz s 0 eng d
020
$a
9783030966232$q(electronic bk.)
020
$a
9783030966225$q(paper)
024
7
$a
10.1007/978-3-030-96623-2
$2
doi
035
$a
978-3-030-96623-2
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
072
7
$a
UYQM
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQM
$2
thema
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.A266 2022
100
1
$a
Aggarwal, Charu C.
$3
264940
245
1 0
$a
Machine learning for text
$h
[electronic resource] /
$c
by Charu C. Aggarwal.
250
$a
Second edition.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
xxiii, 565 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
1 An Introduction to Text Analytics -- 2 Text Preparation and Similarity Computation -- 3 Matrix Factorization and Topic Modeling -- 4 Text Clustering -- 5 Text Classification: Basic Models -- 6 Linear Models for Classification and Regression -- 7 Classifier Performance and Evaluation -- 8 Joint Text Mining with Heterogeneous Data -- 9 Information Retrieval and Search Engines -- 10 Language Modeling and Deep Learning -- 11 Attention Mechanisms and Transformers -- 12 Text Summarization -- 13 Information Extraction and Knowledge Graphs -- 14 Question Answering -- 15 Opinion Mining and Sentiment Analysis -- 16 Text Segmentation and Event Detection.
520
$a
This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.
650
0
$a
Machine learning.
$3
188639
650
0
$a
Data mining.
$3
184440
650
1 4
$a
Machine Learning.
$3
833608
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
275288
650
2 4
$a
Information Storage and Retrieval.
$3
274190
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/978-3-030-96623-2
950
$a
Computer Science (SpringerNature-11645)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
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
000000231753
電子館藏
1圖書
電子書
EB Q325.5 .A266 2022 2022
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-030-96623-2
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入