Granular text classification for biomedical natural language processing

Abdar, Omid

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https://hdl.handle.net/2142/122213 Copy

Description

Title

Granular text classification for biomedical natural language processing

Author(s)

Abdar, Omid

Issue Date

2023-11-15

Director of Research (if dissertation) or Advisor (if thesis)

• Schwartz, Lane
• Stevens, Jon

Doctoral Committee Chair(s)

Schwartz, Lane

Committee Member(s)

• Markee, Numa
• Sadler, Randall
• Ionin, Tania

Department of Study

Linguistics

Discipline

Linguistics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• natural language processing
• NLP
• biomedical natural language processing
• text classification
• transformers
• granularity

Abstract

Text classification is a classic NLP problem with numerous applications and use cases in sentiment analysis, spam detection, document organization, and information retrieval systems. While text classification techniques can be applied to documents of different lengths, supervised machine learning approaches for text classification require labeled documents similar in length to those that will be used at inference. This dissertation examines how labeled documents at higher levels of linguistic granularity (i.e., longer documents) may be synthesized to develop text classifiers at a lower level of linguistic granularity (i.e., shorter text). More specifically, we focus on Biomedical Natural Language Processing as our target domain and address (1) how well document-level classifiers perform for sentence-level classification; (2) how document-level labeled data may be synthesized to perform sentence-level text classification; and (3) how the performance of synthesized approaches compares against benchmark performances using labeled data. We present feature contribution analysis experiments in Naïve Bayes classifiers as well as self-attention experiments in BERT, and report sentence classification results that beat baseline performance for both model types. Finally, we present an extensive qualitative error analysis to identify major error trends in our results and discuss the significance of each error category with respect to our primary research questions.

Graduation Semester

2023-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/122213

Copyright and License Information

Copyright 2023 Omid Abdar

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