Using machine learning and natural language processing to improve scientific processes / Titipat Achakulvisut.

Format:

• Book
• Thesis/Dissertation

Author/Creator:

• Achakulvisut, Titipat, author.

Contributor:

• Kording, Konrad, degree supervisor.
• University of Pennsylvania. Department of Bioengineering, degree granting institution.

Language:

English

Subjects (All):

• Computer science.
• Bioengineering.
• Peer review.
• Scientists.
• Collaboration.
• Datasets.
• Communication.
• Open source software.
• Funding.
• Registration.
• COVID-19.
• Schedules.
• Scheduling.
• Bibliometrics.
• Social interaction.
• Peers.
• Publications.
• Pandemics.
• Decision making.
• Medical research.
• Algorithms.
• Annotations.
• Semantics.
• Bioengineering--Penn dissertations.
• Penn dissertations--Bioengineering.

Local Subjects:

• Computer science.
• Bioengineering.
• Peer review.
• Scientists.
• Collaboration.
• Datasets.
• Communication.
• Open source software.
• Funding.
• Registration.
• COVID-19.
• Schedules.
• Scheduling.
• Bibliometrics.
• Social interaction.
• Peers.
• Publications.
• Pandemics.
• Decision making.
• Medical research.
• Algorithms.
• Annotations.
• Semantics.
• Bioengineering--Penn dissertations.
• Penn dissertations--Bioengineering.

Genre:

• Academic theses.

Physical Description:

1 online resource (159 pages)

Contained In:

Dissertations Abstracts International 83-03B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2021.

Language Note:

English

System Details:

• Mode of access: World Wide Web.
• text file

Summary:

Scientific information has been growing exponentially over the past decades. Arguably, traditional processes of doing science cannot keep up with this growth. This expansion has a scaling impact on scientific activities such as funding, the review process, conferences, and exploring the literature. To improve on the traditional scientific processes, useful tools and understanding of these processes are crucial. This dissertation advances the scientific processes by incorporating knowledge and tools from machine learning (ML) and natural language processing (NLP). We discuss the applications in three applications of scientific endeavors including (1) improving on traditional conferences with data driven approaches, (2) extracting scientific claims for scientific literature, and (3) understanding the funding process using content of applications. To complement our findings, we provided open-source softwares, tools, and real-world implementation for other researchers. In sum, this thesis serves as both a conceptual point of view and a proof-of-concept implementation of how we can improve science through the use of ML and NLP.

Notes:

• Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
• Advisors: Kording, Konrad; Committee members: Ungar, Lyle; Greene, Casey; Fang-Yen, Christopher.
• Department: Bioengineering.
• Ph.D. University of Pennsylvania 2021.

Local Notes:

School code: 0175

ISBN:

9798535569635

Access Restriction:

• Restricted for use by site license.
• This item is not available from ProQuest Dissertations & Theses.
• This item must not be sold to any third party vendors.