A Data Augmentation Approach to Short Text Classification

Autor/in	Rosario, Ryan Robert
Titel	A Data Augmentation Approach to Short Text Classification
Quelle	(2017), (209 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of California, Los Angeles
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3697-0246-0
Schlagwörter	Hochschulschrift; Dissertation; Classification; Models; Semantics; Electronic Publishing; Text Structure; Comparative Analysis; Internet; Computational Linguistics; Form Classes (Languages) + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Semantik; Elektronisches Publizieren; Textstruktur; Linguistics; Computerlinguistik; Analytischer Sprachbau
Abstract	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. 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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01