This is a first public release of the arXMLiv dataset generated by the [KWARC](https://kwarc.info/) research group. Its intended redistribution is confined to the scope of the [SIGMathLing] interest group, and access is members-only..
We welcome community feedback on all of: data quality, need for auxiliary resources (e.g. figures, token models), representation issues, as well as organization and archival best practices.
This is a first public release of the arXMLiv dataset generated by the [KWARC](https://kwarc.info/) research group. It contains 1,088,370 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources.
Next release is planned for mid-2018, with an up-to-date arXiv dataset and community feedback incorporated. We anticipate annual dataset releases going forward.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
- The `no_problem` set had no obvious challenges in conversion and is the safest, most reliable subset
- The `warning` set covers a variety of minor issues, from mathematical expressions unparseable by the LaTeXML grammar, to missing LaTeX packages with no apparent use in the document. The vast majority of the documents should both have a good-looking rendering, as well as data consistency for e.g. NLP tasks.
- The `error` set covers all conversions which successfully generated an HTML5 document, but had major issues during the conversion. Examples would range from unknown macros (due to limited LaTeX coverage), unexpected latex syntax, math/text mode mismatches, as well as real LaTeX errors from the original sources. This subset should be used with extra caution, though should still preserve overall data consistency and could be safely used for e.g. generating word embeddings.
This version of the dataset has had minimal manual quality control, and we offer no additional warranty beyond the latexml severity reported.
We welcome community feedback on all of: data quality, representation issues, need for auxiliary resources (e.g. figures, token models), as well as organization and archival best practices. The conversion, build system, and data redistribution efforts are all ongoing projects at the KWARC research group.
A following release is planned for mid-2018, with an up-to-date arXiv dataset and community feedback incorporated. We anticipate annual dataset releases going forward.
### Citing this Resource
The dataset should be referenced in all academic publications that present results
The dataset should be referenced in all academic publications that present results
obtained with its help. The reference should contain the identifier `arXMLiv:08.2017` in
the title, the author, year, a reference to SIGMathLing, and the URL of the resource
description page. For convenience, we supply some records for bibTeX and EndNote below.
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@@ -50,8 +58,8 @@ description page. For convenience, we supply some records for bibTeX and EndNote
```
@MISC{SML:arXMLiv:08.2017,
author = {Deyan Ginev},
title = {\texttt{arXMLiv:08.2017}},
howpublished = {Data Set at \url{https://sigmathling.kwarc.info/resources/arxmliv/}},
title = {arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org},
howpublished = {hosted at \url{https://sigmathling.kwarc.info/resources/arxmliv/}},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = 2018}
```
...
...
@@ -60,7 +68,7 @@ description page. For convenience, we supply some records for bibTeX and EndNote
```
@online{SML:arXMLiv:08.2017,
author = {Deyan Ginev},
title = {Data Set \texttt{arXMLiv:08.2017}},
title = {arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org},
