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--- |
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license: mit |
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text_categories: |
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- lexical normalization |
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language: |
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- en |
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pretty_name: MaintNorm |
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size_categories: |
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- 10K<n<100K |
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multilingualism: |
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- monolingual |
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--- |
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# MaintNorm Dataset Card |
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## Overview |
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The MaintNorm dataset is a collection of 12,000 English language texts, specifically focusing on short texts extracted from maintenance work orders from three major mining organisations in Australia. This dataset is annotated for both lexical normalization and token-level entity tagging tasks, making it a valuable resource for natural language processing research and applications in industrial contexts. |
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For further information about the annotation process and dataset characteristics, refer to the [MaintNorm paper](https://aclanthology.org/2024.wnut-1.7/) or vitit the [GitHub repository](https://github.com/nlp-tlp/maintnorm) |
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## Dataset Structure |
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This dataset includes data from three distinct company-specific sources (`company_a`, `company_b`, `company_c`), along with a `combined` dataset that integrates data across these sources. This structure supports both granular and comprehensive analyses. |
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## Masking Scheme |
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To address privacy and data specificity, the following token-level entity tags are used: |
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- `<id>`: Asset identifiers, for example, _ENG001_, _rd1286_ |
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- `<sensitive>`: Sensitive information specific to organisations, including proprietary systems, third-party contractors, and names of personnel. |
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- `<num>`: Numerical entities, such as _8_, _7001223_ |
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- `<date>`: Representations of dates, either in numerical form like _10/10/2023_ or phrase form such as _8th Dec_ |
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## Dataset Instances |
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The dataset adopts a standard normalisation format similar to that used in the WNUT shared tasks, with each text resembling the format seen in CoNLL03: tokens are separated by newlines, and each token is accompanied by its normalised or masked counterpart, separated by a tab. |
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### Examples |
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```txt |
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Exhaust exhaust |
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Fan fan |
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#6 number <num> |
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Tripping tripping |
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c/b circuit breaker |
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HF338 <id> |
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INVESTAGATE investigate |
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24V <num> V |
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FAULT fault |
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``` |
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## Citation |
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Please cite the following paper if you use this dataset in your research: |
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``` |
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@inproceedings{bikaun-etal-2024-maintnorm, |
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title = "{M}aint{N}orm: A corpus and benchmark model for lexical normalisation and masking of industrial maintenance short text", |
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author = "Bikaun, Tyler and |
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Hodkiewicz, Melinda and |
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Liu, Wei", |
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editor = {van der Goot, Rob and |
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Bak, JinYeong and |
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M{\"u}ller-Eberstein, Max and |
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Xu, Wei and |
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Ritter, Alan and |
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Baldwin, Tim}, |
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booktitle = "Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)", |
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month = mar, |
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year = "2024", |
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address = "San {\.G}iljan, Malta", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.wnut-1.7", |
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pages = "68--78", |
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} |
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``` |
