ArticleManufacturing letters2021
Technical Language Processing: Unlocking Maintenance Knowledge.
Article in Manufacturing letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Leveraging Active Learning for Failure Mode Acquisition.Sensors (Basel, Switzerland) · 2023Article
- Fusion-Learning of Bayesian Network Models for Fault Diagnostics.Sensors (Basel, Switzerland) · 2021Article
- Adapting natural language processing for technical text.Applied AI letters · 2021Article
- Keyphrase Extraction for Technical Language Processing.Journal of research of the National Institute of Standards and Technology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of engineering data. Text-based documents account for a significant portion of data collected during the life cycle of asset management and the valuable information these documents contain is underutilized in analysis. Meanwhile, researchers historically design NLP pipelines with non-technical language in mind. This means industrial implementations are built on tools intended for non-technical use cases, suffering from a lack of verification, validation, and ultimately, personnel trust. To mitigate these sources of risk, we encourage a holistic, domain-driven approach to using NLP in a technical engineering setting, a paradigm we refer to as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.