عنوان

Multidimensional mining of massive text data /

شابک

1681735202

شابک

9781681735207

شابک اشتباه

1681735199

شابک اشتباه

1681735210

شابک اشتباه

9781681735191

شابک اشتباه

9781681735214

عنوان اصلي

Multidimensional mining of massive text data /

نام عام مواد

[Book]

نام نخستين پديدآور

Chao Zhang, Jiawei Han.

محل نشرو پخش و غیره

[San Rafael, California] :

نام ناشر، پخش کننده و غيره

Morgan & Claypool,

تاریخ نشرو بخش و غیره

[2019]

نام خاص و کميت اثر

1 online resource (1 PDF (xiv, pages)) :

ساير جزييات

illustrations

عنوان فروست

Synthesis lectures on data mining and knowledge discovery,

مشخصه جلد

#17

شاپا ي ISSN فروست

2151-0067 ;

متن يادداشت

Part of: Synthesis digital library of engineering and computer science.

متن يادداشت

Title from PDF title page (viewed on April 2, 2019).

متن يادداشت

Includes bibliographical references (pages 169-181).

متن يادداشت

1. Introduction -- 1.1. Overview -- 1.2. Main parts -- 1.3. Technical roadmap -- 1.4. Organization

متن يادداشت

3. Term-level taxonomy generation / Jiaming Shen -- 3.1. Overview -- 3.2. Related work -- 3.3. Problem formulation -- 3.4. The HiExpan framework -- 3.5. Experiments -- 3.6. Summary

متن يادداشت

4. Weakly supervised text classification / Yu Meng -- 4.1. Overview -- 4.2. Related work -- 4.3. Preliminaries -- 4.4. Pseudo-document generation -- 4.5. Neural models with self-training -- 4.6. Experiments -- 4.7. Summary 69

متن يادداشت

5. Weakly supervised hierarchical text classification / Yu Meng -- 5.1. Overview -- 5.2. Related work -- 5.3. Problem formulation -- 5.4. Pseudo-document generation -- 5.5. The hierarchical classification model -- 5.6. Experiments -- 5.7. Summary

متن يادداشت

7. Cross-dimension prediction in cube space -- 7.1. Overview -- 7.2. Related work -- 7.3. Preliminaries -- 7.4. Semi-supervised multimodal embedding -- 7.5. Online updating of multimodal embedding -- 7.6. Experiments -- 7.7. Summary

متن يادداشت

8. Event detection in cube space -- 8.1. Overview -- 8.2. Related work -- 8.3. Preliminaries -- 8.4. Candidate generation -- 8.5. Candidate classification -- 8.6. Supporting continuous event detection -- 8.7. Complexity analysis -- 8.8. Experiments -- 8.9. Summary

متن يادداشت

9. Conclusions -- 9.1. Summary -- 9.2. Future work.

متن يادداشت

part I. Cube construction algorithms. 2. Topic-level taxonomy generation -- 2.1. Overview -- 2.2. Related work -- 2.3. Preliminaries -- 2.4. Adaptive term clustering -- 2.5. Adaptive term embedding -- 2.6. Experimental evaluation -- 2.7. Summary

متن يادداشت

part II. Cube exploitation algorithms. 6. Multidimensional summarization / Fangbo Tao -- 6.1. Introduction -- 6.2. Related work -- 6.3. Preliminaries -- 6.4. The ranking measure -- 6.5. The RepPhrase method -- 6.6. Experiments -- 6.7. Summary

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional--they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

شماره استاندارد بين المللي کتاب و موسيقي

9781681735191

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Text processing (Computer science)

موضوع مستند نشده

COMPUTERS-- General.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Text processing (Computer science)

موضوع مستند نشده

COM-- 000000

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Zhang, Chao, (Computer scientist)

مستند نام اشخاص تاييد نشده

Han, Jiawei

تاريخ عمليات

20200823052157.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y