Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Word Embeddings for the Construction Domain   We introduce word vectors for the construction domain. Our vectors were
obtained by running word2vec on an 11M-word corpus that we created from scratch
by leveraging freely-accessible online sources of construction-related text. We
first explore the embedding space and show that our vectors capture meaningful
construction-specific concepts. We then evaluate the performance of our vectors
against that of ones trained on a 100B-word corpus (Google News) within the
framework of an injury report classification task. Without any parameter
tuning, our embeddings give competitive results, and outperform the Google News
vectors in many cases. Using a keyword-based compression of the reports also
leads to a significant speed-up with only a limited loss in performance. We
release our corpus and the data set we created for the classification task as
publicly available, in the hope that they will be used by future studies for
benchmarking and building on our work.

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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: physics