Commit 0143cbf9 authored by Michael Kohlhase's avatar Michael Kohlhase

Merge branch 'arxmliv-release' into 'master'

Document 08.2017 arxlmiv dataset release

See merge request !1
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---
layout: post
title: First Data Set (1.1 Million scientific HTML5 documents from arXiv)
---
SIGMathLing has published a first data set, which also acts as a template for future data
sets. The content of this data set is licensed to [SIGMathLing members](/member/) for research
and tool development purposes subject to the [SIGMathLing Non-Disclosure-Agreement](/nda/).
This collection of 1.1 Million HTML5 documents
has been developed as part of the [arXMLiv](https://kwarc.info/systems/arXMLiv/) project at
the [KWARC](https://kwarc.info/) research group. It was created by converting the
[arXiv collection of scientific preprints until August 2017](http://arxiv.org) via
[LaTeXML](https://github.com/brucemiller/LaTeXML) using the
[CorTeX corpus management system](https://github.com/dginev/CorTeX).
Details can be found on the [SIGMathLing Resource page](/resources/arxmliv/).
---
layout: page
title: SIGMathLing - Datasets and Resources
---
none yet, but see the [plan](/technical/)
---
layout: page
title: arXMLiv 08.2017 - An HTML5 dataset for arXiv.org
---
Part of the [arXMLiv](https://kwarc.info/systems/arXMLiv/) project at the [KWARC](https://kwarc.info/) research group
### Author
Deyan Ginev,
### Current release
- 08.2017
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
articles, the right of distribution was only given (or assumed) to arXiv itself.
### Contents
- 1,088,370 HTML5 documents
- Three separate archive bundles separated by LaTeXML conversion severity
- derivative **word embeddings** and a **token model** are available separately [here](/resources/arxmliv-embeddings-082017/)
| subset ID | number of documents | size archived | size unpacked |
| :--- | ---: | ---: | ---: |
| no\_problem| 112,088 | 5 GB | 37 GB |
| warning | 574,638 | 71 GB | 595 GB |
| error | 401,644 | 50 GB | 421 GB |
| subset file name | MD5 |
| :--- | :--- |
| `arXMLiv_08_2017_no_problem.zip` | `036945755c7cc75ea1577cf04ca4fead` |
| `arXMLiv_08_2017_warning.zip` | `c0d5c1baf626225b48264510ac4c6bd5` |
| `arXMLiv_08_2017_error.zip` | `2f4e60b993d85d30523b064c19e45733` |
### Description
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.
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](http://kwarc.info).
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
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. To
cite a particular part of the dataset use the subset identifiers in the ciation; e.g. `
\cite[no_problem subset]{arXMLiv:08.2017}` or just explain it in the text using the
concrete identifier.
#### pure bibTeX
```
@MISC{SML:arXMLiv:08.2017,
author = {Deyan Ginev},
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}
```
#### bibTeX for the bibLaTeX package (preferred)
```
@online{SML:arXMLiv:08.2017,
author = {Deyan Ginev},
title = {arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org},
url = {https://sigmathling.kwarc.info/resources/arxmliv/},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = 2018}
```
#### EndNote
```
%0 Generic
%T arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org
%A Ginev, Deyan
%D 2018
%I hosted at https://sigmathling.kwarc.info/resources/arxmliv/
%F SML:arXMLiv:08.2017b
%O SIGMathLing – Special Interest Group on Math Linguistics
```
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/dataset-arXMLiv-08-2017)
([SIGMathLing members](/member/) only)
### Generated via
- [LaTeXML 0.8.2](https://github.com/brucemiller/LaTeXML/releases/tag/v0.8.2),
- [CorTeX 0.2](https://github.com/dginev/CorTeX/releases/tag/0.2.0)
---
layout: page
title: arXMLiv 08.2017 - Word Embeddings; Token Model
---
Part of the [arXMLiv](https://kwarc.info/systems/arXMLiv/) project at the [KWARC](https://kwarc.info/) research group
### Author
Deyan Ginev,
### Current release
- 08.2017
### Accessibility and License
The content of this Dataset is licensed to [SIGMathLing members](/member/) for research
and tool development purposes.
Access is restricted to [SIGMathLing members](/member/) under the
[SIGMathLing Non-Disclosure-Agreement](/nda/) as for most [arXiv](http://arxiv.org)
articles, the right of distribution was only given (or assumed) to arXiv itself.
### Contents
- A 5 billion token model for the arXMLiv 08.2017 dataset
- `glove.arxmliv.5B.300d.zip` and `vocab.arxmliv.zip`
- 300 dimensional GloVe word embeddings for the arXMLiv 08.2017 dataset
- `token_model.zip`
- subset word embeddings
- `glove.subset.zip`
- the main arXMLiv dataset is available separately [here](/resources/arxmliv-dataset-082017/)
#### Token Model Statistics
subset | documents | paragraphs | sentences |
-----------|----------:|-----------:|------------:|
no_problem | 112,088 | 3,760,015 | 17,684,762 |
warning | 574,638 | 35,215,866 | 144,166,524 |
error | 401,644 | 28,555,173 | 111,798,273 |
complete | 1,088,370 | 67,531,054 | 273,649,559 |
subset | words |formulas | inline cite | numeric literals |
-----------|--------------:|-----------:|------------:|-----------------:|
no_problem | 355,253,671 |17,020,161 | 2,991,053 | 9,913,009 |
warning | 2,514,340,590 |219,167,820 | 20,163,304 | 65,294,846 |
error | 1,946,207,151 |169,247,016 | 14,458,082 | 51,730,645 |
complete | 4,815,801,412 |405,434,997 | 37,612,439 | 126,938,500 |
#### GloVe Model Statistics
subset | tokens | unique words | unique words (freq 5+ )
-----------|--------------:|-------------:|-----------------------:
no_problem | 384,951,086 | 490,134 | 170,615
warning | 2,817,734,902 | 1,200,887 | 422,524
error | 2,180,119,361 | 1,889,392 | 518,609
complete | 5,382,805,349 | 2,573,974 | 746,673
### Citing this Resource
Please cite the main dataset when using the word embeddings, as they are generated and distributed jointly. [Instructions here](/resources/arxmliv-dataset-082017/#citing-this-resource)
### Download
[Download link](https://gl.kwarc.info/SIGMathLing/embeddings-arXMLiv-08-2017)
([SIGMathLing members](/member/) only)
### Generated via
- [llamapun 0.1](https://github.com/KWARC/llamapun/releases/tag/0.1),
- [GloVe 1.2](https://github.com/stanfordnlp/GloVe/tree/765074642a6544e47849bb85d8dc2e11e44c2922)
### Generation Parameters
* token model distributed as 3 subsets - no_problem, warning and error. complete model is derived via:
```
cat token_model_no_problem.txt \
token_model_warning.txt \
token_model_error.txt > token_model_complete.txt
```
* [llamapun v0.1](https://github.com/KWARC/llamapun/releases/tag/0.1), `corpus_token_model` example used for token model extraction
* used llamapun math-aware sentence and word tokenization
* processed logical paragraphs only (excluded non-textual modalities, e.g. tables, figures and their captions, bibliographies)
* marked up formulas replaced with `mathformula` token
* marked up inline citations replaced with `citationelement` token
* numeric literals replaced with `NUM` token
* ignored sentences with unnaturally long words (>30 characters) - almost always due to latexml conversion errors
* [GloVe repository at sha 76507](https://github.com/stanfordnlp/GloVe/tree/765074642a6544e47849bb85d8dc2e11e44c2922)
* build/vocab_count -min-count 5
* build/cooccur -memory 32.0 -window-size 15
* build/shuffle -memory 32.0
* build/glove -threads 16 -x-max 100 -iter 25 -vector-size 300 -binary 2
### Examples and baselines
#### GloVe in-built evaluation (non-expert tasks e.g. language, relationships, geography)
1. no_problem
* Total accuracy: 26.49% (3665/13833)
* Highest score: "gram3-comparative.txt", 75.83% (1010/1332)
2. warning
* Total accuracy: 31.16% (4989/16013)
* Highest score: "gram3-comparative.txt", 75.45% (1005/1332)
3. error
* Total accuracy: 29.63% (4997/16867)
* Highest score: "gram3-comparative.txt", 76.58% (1020/1332)
4. complete
* Total accuracy: 32.86% (5770/17562)
* Highest score: "gram3-comparative.txt", 78.53% (1046/1332)
5. demo baseline: text8 demo (first 100M characters of Wikipedia)
* Total accuracy: 23.91% (4262/17827)
* Highest score: "capital-common-countries.txt", 62.65% (317/506)
**Evaluation note:** These in-built evlauation runs are provided as a sanity check that the generated GloVe models pass a basic baseline against the non-expert tasks in the default GloVe suite.
One would need a scienctific discourse tailored set of test cases to evaluate the arXiv-based models competitively.
#### Measuring word analogy
In a cloned GloVe repository, start via:
```
python eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt
```
1. `abelian` is to `group` as `disjoint` is to `?`
* Top hit: `union`, cosine distance `0.644784`
2. `convex` is to `concave` as `positive` is to `?`
* Top hit: `negative`, cosine distance `0.802866`
3. `finite` is to `infinte` as `abelian` is to `?`
* Top hit: `nonabelian`, cosine distance `0.664235`
4. `quantum` is to `classical` as `bottom` is to `?`
* Top hit: `top`, cosine distance `0.719843`
5. `eq` is to `proves` as `figure` is to `?`
* Top hit: `shows`, cosine distance `0.674743`
#### Nearest word vectors
In a cloned GloVe repository, start via:
```
python eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt
```
1. **lattice**
```
Word: lattice Position in vocabulary: 311
Word Cosine distance
-----------------------------------------------------
lattices 0.811057
honeycomb 0.657262
finite 0.625146
triangular 0.608218
spacing 0.605435
```
2. **entanglement**
```
Word: entanglement Position in vocabulary: 1293
Word Cosine distance
-----------------------------------------------------
entangled 0.763964
multipartite 0.730231
fidelity 0.653443
concurrence 0.652454
environemtnal 0.646705
negativity 0.646165
quantum 0.639032
discord 0.624222
nonlocality 0.610661
tripartite 0.609896
```
3. **forgetful**
```
Word: forgetful Position in vocabulary: 10697
Word Cosine distance
-----------------------------------------------------
functor 0.723019
functors 0.653969
morphism 0.626222
```
4. **eigenvalue**
```
Word: eigenvalue Position in vocabulary: 1212
Word Cosine distance
-----------------------------------------------------
eigenvalues 0.878527
eigenvector 0.766371
eigenfunction 0.761923
eigenvectors 0.747451
eigenfunctions 0.707346
eigenspace 0.661539
corresponding 0.629746
laplacian 0.627187
operator 0.627130
eigen 0.620933
```
5. **riemannian**
```
Word: riemannian Position in vocabulary: 2026
Word Cosine distance
-----------------------------------------------------
manifold 0.766196
manifolds 0.745785
metric 0.714120
curvature 0.672975
metrics 0.670006
finsler 0.665079
ricci 0.657058
euclidean 0.650198
endowed 0.626307
riemmanian 0.621626
riemanian 0.618022
```
\ No newline at end of file
---
layout: page
title: SIGMathLing - Datasets and Resources
---
1. [arXMLiv corpus, 08.2017 release](/resources/arxmliv-dataset-082017/)
2. [arXMLiv word embeddings, 08.2017 release](/resources/arxmliv-embeddings-082017)
Additional resources are en route, see the [plan](/technical/) for details.
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