Natural language processing in-and-for design research

Abstract

We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

The innovation paradox: concept space expansion with diminishing originality and the promise of creative artificial intelligence

“…More recent studies have leveraged large pre-trained lexical databases and natural language processing (NLP) techniques to automatically assess the originality of newly generated design concepts in natural language descriptions (Siddharth et al 2022 a ). Siddharth, Madhusudanan & Chakrabarti (2020) propose a method to evaluate the novelty of a design solution, based on its distance to a reference product database.…”

Human-AI collaboration by design

“…In the field of design innovation, AI's role in analysis is critical and multifaceted. It begins with understanding user needs, which includes analysing online reviews to decipher consumer preferences and insights (Siddharth et al, 2022). This extends to retrieving technical knowledge through knowledgebased systems (Siddharth et al, 2022;Luo et al, 2019), aiding informed decision-making in design.…”

Visualizing design project team and individual progress using NLP: a comparison between latent semantic analysis and Word2Vector algorithms

“…The use of text-based data mining processes in attempt to understand the conversations about projects and to produce summaries through various NLP techniques are widely adopted through various means (Dong et al, 2004;Chiu et al, 2022;Ferguson et al, 2022). Design as a process at its various stage revolves around tons of text documentations such as internal reports, design concepts, discourse transcripts, and technical publication where tools like NLP and AI can be utilized gather insights or predict recommendations (Koh, 2022;Siddharth et al, 2022). However, most of such data mining processes crawl through untargeted data source such as communication chat groups or general documents revolving around the project such as emails or reports.…”