  We introduce the first automated models for classifying natural language
descriptions provided in cost documents called "Bills of Quantities" (BoQs)
popular in the infrastructure construction industry, into the International
Construction Measurement Standard (ICMS). The models we deployed and
systematically evaluated for multi-class text classification are learnt from a
dataset of more than 50 thousand descriptions of items retrieved from 24 large
infrastructure construction projects across the United Kingdom. We describe our
approach to language representation and subsequent modelling to examine the
strength of contextual semantics and temporal dependency of language used in
construction project documentation. To do that we evaluate two experimental
pipelines to inferring ICMS codes from text, on the basis of two different
language representation models and a range of state-of-the-art sequence-based
classification methods, including recurrent and convolutional neural network
architectures. The findings indicate a highly effective and accurate ICMS
automation model is within reach, with reported accuracy results above 90% F1
score on average, on 32 ICMS categories. Furthermore, due to the specific
nature of language use in the BoQs text; short, largely descriptive and
technical, we find that simpler models compare favourably to achieving higher
accuracy results. Our analysis suggest that information is more likely embedded
in local key features in the descriptive text, which explains why a simpler
generic temporal convolutional network (TCN) exhibits comparable memory to
recurrent architectures with the same capacity, and subsequently outperforms
these at this task.
