Abstract
Within any sufficiently expertise-reliant and work-driven domain there is a requirement to understand the similarities between specific work tasks. Though mechanisms to develop similarity models for these areas do exist, in practice they have been criticised within various domains by experts who feel that the output is not indicative of their viewpoint. In field service provision for telecommunication organisations, it can be particularly challenging to understand task similarity from the perspective of an expert engineer. With that in mind, this paper demonstrates a similarity model developed from text recorded by engineer’s themselves to develop a metric directly indicative of expert opinion. We evaluate several methods of learning text representations on a classification task developed from engineers’ notes. Furthermore, we introduce a means to make use of the complex and multi-faceted aspect of the notes to recommend additional information to support engineers in the field.
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Notes
- 1.
Both SNN sub-network architectures were comprised of 3-layer perceptrons which used an SGD optimizer, ReLU activations and were trained for 250 epochs.
- 2.
The displayed results for SNNs used Doc2Vec embeddings as input, as they performed better. When using tf-idf representations as input, both SNNs still outperformed tf-idf and Doc2Vec.
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Martin, K., Liret, A., Wiratunga, N., Owusu, G., Kern, M. (2018). Risk Information Recommendation for Engineering Workers. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_27
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