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A Data Augmentation Approach to Shor...

University of California, Los Angeles.

A Data Augmentation Approach to Short Text Classification.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Data Augmentation Approach to Short Text Classification./
作者:	Rosario, Ryan Robert.
面頁冊數:	1 online resource (209 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781369702460

A Data Augmentation Approach to Short Text Classification.
Rosario, Ryan Robert.

A Data Augmentation Approach to Short Text Classification. - 1 online resource (209 pages)

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2017.

Includes bibliographical references

Text classification typically performs best with large training sets, but short texts are very common on the World Wide Web. Can we use resampling and data augmentation to construct larger texts using similar terms? Several current methods exist for working with short text that rely on using external data and contexts, or workarounds.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369702460Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

A Data Augmentation Approach to Short Text Classification.
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100 1 $a Rosario, Ryan Robert. $3 1186821
245 1 2 $a A Data Augmentation Approach to Short Text Classification.
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500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
500 $a Adviser: Yingnian Wu.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2017.
504 $a Includes bibliographical references
520 $a Text classification typically performs best with large training sets, but short texts are very common on the World Wide Web. Can we use resampling and data augmentation to construct larger texts using similar terms? Several current methods exist for working with short text that rely on using external data and contexts, or workarounds.
520 $a Our focus is to test a new preprocessing approach that uses resampling, inspired by the bootstrap, combined with data augmentation, by treating each short text as a population and sampling similar words from a semantic space to create a longer text. We use blog post titles collected from the Technorati blog aggregator as experimental data with each title appearing in one of ten categories. We first test how well the raw short texts are classified using a variant of SVM designed specifically for short texts as well as a supervised topic model and an SVM model that uses semantic vectors as features. We then build a semantic space and augment each short text with related terms under a variety of experimental conditions. We test the classifiers on the augmented data and compare performance to the aforementioned baselines. The classifier performance on augmented test sets outperformed the baseline classifiers in most cases.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Computer science. $3 573171
650 4 $a Linguistics. $3 557829
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a University of California, Los Angeles. $b Statistics. $3 1186550
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