Maintenance short texts are invaluable unstructured data sources, serving as a diagnostic and prognostic window into the operational health and status of physical assets. These user-generated texts, created during routine or ad-hoc maintenance activities, offer insights into equipment performance, potential failure points, and maintenance needs. However, the use of information captured in these texts is hindered by inherent challenges: the prevalence of engineering jargon, domain-specific vernacular, random spelling errors without identifiable patterns, and the absence of standard grammatical structures. To transform these texts into accessible and analysable data, we introduce the MaintNorm dataset, the first resource specifically tailored for the lexical normalisation task of maintenance short texts. Comprising 12,000 examples, this dataset enables the efficient processing and interpretation of these texts. We demonstrate the utility of MaintNorm by training a lexical normalisation model as a sequence-to-sequence learning task with two learning objectives, namely, enhancing the quality of the texts and masking segments to obscure sensitive information to anonymise data. Our benchmark model demonstrates a universal error reduction rate of 95.8%. The dataset and benchmark outcomes are available to the public.

Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. Adoption of unsupervised techniques for automated annotation have thus become popular. However, these techniques rely heavily on dictionaries, gazetteers, and knowledge bases. While such resources are abundant for general domains, they are scarce for specialised technical domains. To tackle this challenge, we present QuickGraph, the first collaborative MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. QuickGraph’s main contribution is a set of novel features that enable knowledge graph extraction through rapid and consistent complex multi-task entity and relation annotation. In this paper, we discuss these key features and qualitatively compare QuickGraph to existing annotation tools.

NLP systems are often challenged by difficulties arising from noisy, non-standard, and domain specific corpora. The task of lexical normalisation aims to standardise such corpora, but currently lacks suitable tools to acquire high-quality annotated data to support deep learning based approaches. In this paper, we present LexiClean, the first open-source web-based annotation tool for multi-task lexical normalisation. LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide. We demonstrate the usefulness of our tool through a case study on two sets of noisy corpora derived from the specialised-domain of industrial mining. We show that LexiClean allows for the rapid and efficient development of high-quality parallel corpora. A demo of our system is available at: https://youtu.be/P7_ooKrQPDU.