What are Some Research 

Concerns of TLP?

Alden Dima

Information Technology Laboratory

National Institute of Standards and Technology

April 16th, 2021
Outline

• Motivation

• Strawman proposal for framing TLP

• Different perspectives and potential research questions:

1. Technical Domains

2. Actionable/Business Intelligence

3. Text Mining and Analysis

4. Human–Computer Interaction

5. Linguistics

• Closing thoughts
Michael P. Brundage, Thurston Sexton, Melinda Hodkiewicz, Alden Dima, and Sarah Lukens 

Technical language processing: Unlocking maintenance knowledge,

Manufacturing Letters 27, 42–46, Jan. 2021. DOI: 10.1016/j.mfglet.2020.11.001

Alden Dima, Sarah Lukens, Melinda Hodkiewicz, Thurston Sexton, and Michael P. 

Brundage Adapting natural language processing for technical text,

to appear in Applied AI Letters 

Technical Language Processing (TLP)
What Sets Apart TLP Research?

• How do we distinguish our focus with other research efforts?

• What are our boundaries?

• What kinds of questions should we be asking?

• For example, here is an example of how NLP is described:

“The ultimate goal of natural language processing is for computers to achieve human-like 

comprehension of texts/languages. When this is achieved, computer systems will be able to 

understand, draw inferences from, summarize, translate and generate accurate and natural 

human text and language.”

https://www.expert.ai/blog/natural-language-processing

• Not sure that this is our goal...
NLP and TLP

• There’s no denying that NLP plays a critical role in TLP

• But TLP has a different focus than “human-like comprehension of 

texts/languages”
Framing TLP – A Strawman Proposal

• Focus:

• People working in technical roles within an organization who use dedicated 

computer-mediated text-based communication while participating in 

technical processes related to the organization’s business model

• Excludes communication that:

• Is not related to a technical process as part of organizational business 

model

• Is not computer-mediated or text-based – voicemail, paperwork

• Does not use a dedicated channel – emails to personal mail accounts

• Realize that this may need to be relaxed or modified
Perspective: Technical Domains

• TLP is applicable to a variety of domains, for example:

• Maintenance involves functional checks, servicing, repairing or replacing of 

necessary devices, equipment, machinery, building infrastructure, and 

supporting utilities in industrial, business, and residential installation

Wikipedia: Maintenance (technical)
Questions: Technical Domains

• How can the analysis of technical text address problems in a 

technical domain?

• What types of data needs to be available to address specific 

technical problems?

• How do business procedures need to be modified to support them?

• How do we demonstrate the value of technical text in a particular 

domain?
Perspective: Actionable/Business Intelligence

• Actionable intelligence is the means of obtaining concrete 

knowledge that permits an individual to take action based on 

known information 

Carroll, Christopher J. 2008. “Transforming Data and Metadata into Actionable Intelligence and 

Information within the Maritime Domain.” Monterey, California. Naval Postgraduate School.

• Business intelligence comprises the strategies and technologies 

used by enterprises for the data analysis of business information to 

provide historical, current, and predictive views of business 

operations

Wikipedia: Business intelligence
Questions: Actionable/Business Intelligence

• How can the analysis of technical text be integrated with ObserveOrient-Decide-Act (OODA) strategies to reduce operational risks?

• How can business intelligence technologies use technical text to 

improve organizational decision making?
Perspective: Text Mining and Analysis

• Text mining is the process of deriving high-quality information from text. 

It involves “the discovery by computer of new, previously unknown 

information, by automatically extracting information from different 

written resources.”

• Text analysis involves information retrieval, lexical analysis to study word 

frequency distributions, pattern recognition, tagging/annotation, 

information extraction, data mining techniques including link and 

association analysis, visualization, and predictive analytics. The goal is to 

turn text into data for analysis, via application of NLP, different types of 

algorithms and analytical methods.

Wikipedia: Text mining

Underlined items are things that we normally think about.
Questions: Text Mining and Analysis

• What types of information can we reliably extract from the 

technical text contained in information systems?

• What types of information are the most problematic?

• What affordances does technical data provide that we can 

leverage?

• How do we adapt NLP methods to TLP?

• How can we best use SOTA NLP methods in TLP? When should we?

• What types of technologies and architectures should we use?
Perspective: Human–Computer Interaction

• Human–computer interaction (HCI) studies the design and use of computer technology, focused on the interfaces between people (users) 

and computers. Researchers in the field of HCI observe the ways in which 

humans interact with computers and design technologies that let 

humans interact with computers in novel ways.

• Social computing: there has been an explosion of social science research focusing on interactions as the unit of analysis.

• In human and computer interactions, a semantic gap usually exists 

between human and computer's understandings towards mutual 

behaviors. Ontology, as a formal representation of domain-specific knowledge, can be used to address this problem, through solving the 

semantic ambiguities between the two parties.

Wikipedia: Human–computer interaction
Questions: Human–Computer Interaction

• What is the best strategies for enabling human-in-the-loop TLP 

analyses?

• How do we accelerate and improve the quality of annotation?

• What is the impact human/computer interaction: 

• On organizational processes?

• On the technical text that is entered into technical information systems?

• On the quality of TLP analyses?

• How do we design information systems that improve the 

organizational value of technical text?
Perspective: Linguistics

• In the functional paradigm a language is in the first place 

conceptualized as an instrument of social interaction among human 

beings, used with the intention of establishing communicative 

relationships. Within this paradigm one attempts to reveal the 

instrumentality of language with respect to what people do and 

achieve with it in social interaction.

Wikipedia: Functional linguistics

• In sociolinguistics, a register is a variety of language used for a 

particular purpose or in a particular communicative situation

Wikipedia: Register (sociolinguistics)
Questions: Linguistics

• How do personnel communicate to each other while performing their technical 

roles?

• What information gets communicated and what is omitted and why?

• How much of the omitted information is either assumed or is available 

elsewhere?

• Can we reliably characterize and identify technical text?

• What varieties of expression are present? 

• Can we characterize and identify them?

• How do these linguistic concerns affect the value of text information?
Closing Thoughts

• Not claiming that the proposed focus, perspectives, and research 

problems presented here are either complete or correct

• But it should be clear that TLP is a multidisciplinary field that can benefit 

from a wide range of disciplines

• Hoping to start an ongoing discussion about the scope and goals of 

TLP as its own subfield that engages diverse perspectives from a 

variety of communities

TLP-COI on Slack
Thank you!

Alden Dima

alden.dima@nist.gov