Whittemore Library: Student and Faculty Guide to Artificial Intelligence : Natural Language Processing

NLP

Image created using ChatGPT from OpenAI, February 2025

Natural Language Processing (NLP) is the ability of a computer program to understand human language. This can include written text and speech. NLP is a core component of AI because AI programs use NLP to perform their functions. For example, AI media generators process written text prompts to generate a product.

NLP Functions

NLP allows programs to perform functions such as text analysis & summary, data collection from text, analyzing emotional sentiment in text, and answering user questions. The more sophisticated the NLP abilities of a program are, the more sophisticated its functions will be.

Media Resources

Linked Resources

Applications of NLP for SDOH
A research protocol that details a systematic review used to identify NLP techniques in the study of Social Determinants of Health.
Early Identification of Language Disorders Using NLP
A review article that presents the discussion about how the use of NLP can potentially help to expedite the diagnosis and treatment of language disorders.
Identification of Cultural Conversations in Therapy Using NLP
A discussion about how NLP can be used to analyze therapists' multicultural competency in addition to client responses.

Book Resources

Natural Language Processing by Zoran Gacovski (Editor)
ISBN: 9781774079799
Publication Date: 2020-11-01
This edition covers different topics from natural language processing, including natural language processing in IT / web systems, semantics in natural language processing, mathematical algorithms in natural language processing, and natural language in mobile systems. Section 1 focuses on natural language processing in IT and web systems, describing semantic analysis of natural language queries for an object oriented database; communication mediated through natural language generation in big data environments; web semantic and ontology; and understanding creative language in tweets. Section 2 focuses on semantics in natural language processing, describing resolving topic-focus ambiguities in natural language; semantic interoperability in e-health for improved healthcare; computational context modeling framework - an ontological approach to develop context-aware web applications; and three types of episodic associations for the semantic/syntactic/episodic model of language prospective in applications to the statistical translation. Section 3 focuses on mathematical algorithms in natural language processing, describing bridging between natural language and mathematical language in solving problems in mathematics; Spanish language grammatical context--acknowledging specific language characteristics; shallow parsing approach to natural language queries of a database; comparative study to understanding about poetics based on natural language processing; computers and language learning. Section 4 focuses on natural language in mobile systems, describing automatic approach of sentiment lexicon generation for mobile shopping reviews; sentence-level neural network models for multiple-choice reading comprehension tasks; bibliometric review of natural language processing empowered mobile computing; mobile-based question-answering and early warning system for assisting diabetes management; research on new English mobile teaching mode under the impact of mobile internet age.