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Neural Keyphrase Generation for Social Media Understanding.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural Keyphrase Generation for Social Media Understanding./
作者:	Wang, Yue.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	213 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Web studies. -
ISBN:	9798492724078

Neural Keyphrase Generation for Social Media Understanding.
Wang, Yue.

Neural Keyphrase Generation for Social Media Understanding. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 213 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.

Social media platforms, such as microblogging services and online forums, are becoming increasingly popular, profoundly revolutionizing how people share information and voice opinions. Due to the wide availability of mobile devices and easy connectivity, millions of user-generated messages are produced on a daily basis, leading us to the information explosion era. As a result, the current decade has witnessed a pressing demand for automatically digesting the large volume of social media data and discovering its crucial content. To this end, keyphrase prediction, which aims to summarize a social media post into a set of succinct keywords (or hashtags), receives growing attention in the social media research community.Previous progress made in this field has mainly focused on either extraction-based or classification-based approaches, which are limited in that they cannot predict keyphrases absent in the source text or the predefined candidate list. To overcome this limitation, in this thesis, we study neural keyphrase generation methods that enable new keyphrases to be created for social media posts. In contrast to early methods relying on handcrafted features, we take advantage of recent advances in deep learning and employ neural network-based frameworks that allow effective representation learning in a data-driven manner. More importantly, to alleviate the data sparsity issue widely exhibited in unstructured social media posts, we propose to enrich contexts via either implicitly exploiting the post-level latent topics or explicitly leveraging the user replies or the accompanying images.

ISBN: 9798492724078Subjects--Topical Terms:

2122754
Web studies.
Subjects--Index Terms:

Neural keyphrase

Neural Keyphrase Generation for Social Media Understanding.
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500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Chan, Lai Wan.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.
520 $a Social media platforms, such as microblogging services and online forums, are becoming increasingly popular, profoundly revolutionizing how people share information and voice opinions. Due to the wide availability of mobile devices and easy connectivity, millions of user-generated messages are produced on a daily basis, leading us to the information explosion era. As a result, the current decade has witnessed a pressing demand for automatically digesting the large volume of social media data and discovering its crucial content. To this end, keyphrase prediction, which aims to summarize a social media post into a set of succinct keywords (or hashtags), receives growing attention in the social media research community.Previous progress made in this field has mainly focused on either extraction-based or classification-based approaches, which are limited in that they cannot predict keyphrases absent in the source text or the predefined candidate list. To overcome this limitation, in this thesis, we study neural keyphrase generation methods that enable new keyphrases to be created for social media posts. In contrast to early methods relying on handcrafted features, we take advantage of recent advances in deep learning and employ neural network-based frameworks that allow effective representation learning in a data-driven manner. More importantly, to alleviate the data sparsity issue widely exhibited in unstructured social media posts, we propose to enrich contexts via either implicitly exploiting the post-level latent topics or explicitly leveraging the user replies or the accompanying images.
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