Keyphrase extraction: from distributional feature engineering to distributed semantic composition

Rui Wang

doi:10.4225/23/5a6547885d4f6

Keyphrase extraction: from distributional feature engineering to distributed semantic composition

Rui Wang

Research output: Thesis › Doctoral Thesis

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Abstract

Automatic Keyphrase Extraction is the fundamental step underpinning many Natural Language Processing tasks. However,
the performance of the current solutions heavily relies on the selection of features, rendering the algorithms to be dataset
dependent and failing to capture the semantic meanings of phrases and documents. This research investigated extensively on
the current solutions and built on the most recent success of deep neural networks in natural language processing to mimic the
human cognitive process of keyphrase identification. A number of novel models to learn the meaning of phrases and documents
have been developed and evaluated, achieving the state-of-the-art performance.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Liu, Wei, Supervisor McDonald, Chris, Supervisor
Thesis sponsors	Australian Government Research Training Program
Award date	9 Jan 2018
DOIs	https://doi.org/10.4225/23/5a6547885d4f6
Publication status	Unpublished - 2017

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10.4225/23/5a6547885d4f6

THESIS - DOCTOR OF PHILOSOPHY - WANG Rui - 2017
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Cite this

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