From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research

ABSTRACT

Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between humans and computers. Although there is a large body of NLP studies published in Information Systems (IS), a comprehensive review of how NLP research is conceptualised and realised in the context of IS has not been conducted. To assess the current state of NLP research in IS, we use a variety of techniques to analyse a literature corpus comprising 356 NLP research articles published in IS journals between 2004 and 2018. Our analysis indicates the need to move from semantics to pragmatics. More importantly, our findings unpack the challenges and assumptions underlying current research trends in NLP. We argue that overcoming these challenges will require a renewed disciplinary IS focus. By proposing a roadmap of NLP research in IS, we draw attention to three NLP research perspectives and present future directions that IS researchers are uniquely positioned to address.

ACCEPTING EDITOR:

• Pär Ågerfalk

ASSOCIATE EDITOR:

• Remko Helms

KEYWORDS:

• Natural Language Processing
• natural language generation
• natural language understanding
• pragmatics
• semantics
• disciplinary IS research
• NLP roadmap

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. It is to be noted that the seminal article on design science research by Hevner et al. (2004) was published after the reviews conducted by Palvia et al. (2003).

2. Given the growing prominence of DSR in IS, its inclusion as an important research methodology is critical, and an update to Palvia et al. (2003) categorisation to address this omission is highly warranted.

3. Palvia et al. (2003) conducted a comprehensive review of IS articles published during a five-year period (1993–1997) and identified 12 most methodologies used by seven leading MIS journals (Table 1, pp. 291). Palvia et al. (2004) presented an updated list of methodologies that included “content analysis”. However, since the definition of “content analysis” would cover the spectrum of NLP research, it was not included in our coding.

4. We created the grammatical text analysis task to include a cluster of methods to analyse sentence structure. In all the books we reviewed, related methods (e.g., statistical parsing, part-of-speech tagging) are discussed separately.