A questionnaire integration system based on question classification and short text semantic textual similarity

Citation

Qiu, Yu (2018). A questionnaire integration system based on question classification and short text semantic textual similarity.

Abstract

Semantic integration from heterogeneous sources involves a series of NLP tasks. Existing re- search has focused mainly on measuring two paired sentences. However, to find possible identical texts between two datasets, the sentences are not paired. To avoid pair-wise comparison, this thesis proposed a semantic similarity measuring system equipped with a precategorization module. It applies a hybrid question classification module, which subdivides all texts to coarse categories. The sentences are then paired from these subcategories. The core task is to detect identical texts between two sentences, which relates to the semantic

URL

http://libproxy.lib.unc.edu/login?url=https://search.proquest.com/docview/2164370085?accountid=14244

Keyword(s)

Applied sciences

Notes

ProQuest document ID 2164370085

Reference Type

Thesis/Dissertation

Book Title

Computer Science

Author(s)

Qiu, Yu

Series Author(s)

Lee Pallickara, Sangmi

Year Published

2018

Publisher

Colorado State University

City of Publication

Ann Arbor, MI

ISSN/ISBN

9780438788510

DOI