Application of Natural Language Processing to Unstructured Data: A Case Study of Climate Change

With the ascension of the Information Age and the widespread use of the Internet, a plethora of knowledge exists apropos of numerous areas of interest. The resurgence of big data and machine learning has brought a high hope that designers can learn from past successes and failures. However, when the available data is in a mixture of textual, numerical or graphical form, then the currently popular deep learning tools cannot be applied directly. The question today is about the ability to represent this heterogeneous form of data and to find the relevant information from a huge depository of data in an efficient manner. My study of data preparation is a part of a big group effort in applying Artificial Intelligence based Natural Language Processing models to large corpora of technical design documentation such as climate change reports, which then enable the retrieval of accurate information via semantic search capabilities. The methodology was able to successfully retrieve suitable answers to the user’s questions without reading hundreds of pages of reports. Additionally, the query process was able to bring up Figures and Tables that provided meaningful context to the answers via associate data-linking during the data reading and embedding process.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Ceylan, Ceylan

Advisor dc:contributor.advisor

• Kim, Sang-Gook

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144647

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144647