[Linux] Find, Extract String (arXiv Numbers) from File

I needed to find and grab arXiv article identifiers from a file (e.g., the “1206.5538” in “arXiv:1206.5538” or “https://arxiv.org/abs/1206.5538” or “https://arxiv.org/pdf/1206.5538.pdf”), so that I could bulk import them into JabRef. [JabRef is a Graphical Java application for managing bibtex (.bib) databases.]

Here is my solution.

TASK: find/return specific string in file

SOLUTION:

cat arxiv_test.txt | sed -r 's/^.*arxiv.{9}//p' | egrep -o '[0-9]{4}\.[0-9]{4,5}' | sort -u

Explanation:

Employs regex expressions with the sed and egrep commands:

`^.*arxiv.{9}`	match from beginning of line (`^.`) to “`arxiv`” plus any following 9 (`.{9}`) characters
`p`	print line
`egrep`	same as `grep -E` (`-E`, `--extended-regexp`: interpret PATTERN as an extended regular expression)
`(e)grep -o`	return the `--only-matching` part of the expression (not the entire line, as normally done by grep)
`[0-9]{4}\.[0-9]{4,5}`	match 4 digits, a period, then 4 or 5 digits
`sort -u`	sort the results, return `--unique` matches

TEST FILE:

<!--
$ cat arxiv_test.txt
Natural language processing (NLP), a branch of machine learning (ML), is foundational to all information extraction and natural language tasks. Recent reviews of NLP relevant to this TECHNICAL REVIEW include:

>* [Natural Language Processing for Information Extraction](https://arxiv.org/pdf/1807.02383.pdf)  (Jul 2018);
  >* [Event Extraction for Systems Biology by Text Mining the Literature](https://www.ncbi.nlm.nih.gov/pubmed/20570001)   (Jul 2010);
  >* [Natural Language Processing: An Introduction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168328/)  (Sep-Oct 2011);
  >* [A Biomedical Information Extraction Primer for NLP Researchers](https://arxiv.org/pdf/1705.05437.pdf)  (May 2017);
  >* [Advances in Natural Language Processing](https://www.ncbi.nlm.nih.gov/pubmed/26185244)  (Jul 2017); and,
  >* [Recent Trends in Deep Learning Based Natural Language Processing](https://arxiv.org/pdf/1708.02709.pdf)  (Aug 2018).

... During the course of preparing this REVIEW, highly-relevant literature and developments appeared almost daily on [arXiv.org](https://arxiv.org/), my RSS feeds, and other sources.  I firmly believe that this rapid pace of progress represents outstanding research opportunities rather than barriers (e.g., proposing ML research that may quickly become "dated").

https://arxiv.org/pdf/1301.3781.pdf

------------------------------------------------------------------------------

GRAPH SIGNAL PROCESSING

## References / Additional Reading

See also:

>* [Alex Smola] [Kernels and Regularization on Graphs](http://people.cs.uchicago.edu/~risi/papers/SmolaKondor.pdf) (2003)  [<a href="/files/misc/Smola-2003.pdf">local copy</a>;  <a href="/files/misc/Smola-2003-slides.pdf">slides</a>]
>* [arXiv] [A Tutorial on Spectral Clustering](https://arxiv.org/pdf/0711.0189.pdf) (Nov 2007)
>* [Wavelets on Graphs via Spectral Graph Theory](https://www.sciencedirect.com/science/article/pii/S1063520310000552) (2011)  [<a href="/files/misc/Hammond-2011.pdf">local copy</a>]
> * The wavelet approach from this paper was later used (same authors in each of the following)
> * [Graph Convolutional Neural Networks via Scattering](https://arxiv.org/pdf/1804.00099.pdf) (Mar 2018)
> * [Encoding Robust Representation for Graph Generation](https://arxiv.org/pdf/1809.10851.pdf) (Sep 2018)
>* [arXiv] [Discrete Signal Processing on Graphs](https://arxiv.org/pdf/1210.4752.pdf) (Dec 2012)
>* [Discrete Signal Processing on Graphs: Graph Fourier Transform](https://users.ece.cmu.edu/~asandryh/papers/icassp13a.pdf) (2013)
>* [arXiv] [The Emerging Field of Signal Processing on Graphs](https://arxiv.org/pdf/1211.0053.pdf) (Mar 2013)
>* [arXiv: Yann LeCun] [Spectral Networks and Deep Locally Connected Networks on Graphs](https://arxiv.org/pdf/1312.6203.pdf) (May 2014)
>* [Bandlimited graph signal reconstruction by diffusion operator](https://link.springer.com/content/pdf/10.1186%2Fs13634-016-0421-4.pdf) (2016)
>* [arXiv: Yann LeCun] [Geometric Deep Learning: Going Beyond Euclidean Data](https://arxiv.org/pdf/1611.08097.pdf) (May 2017)
>* [When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum](https://miplab.epfl.ch/pub/vandeville1701.pdf) (Jul 2017)
>* [[M.Sc. thesis?](http://ikee.lib.auth.gr/record/294952/)] [Spectral Graph Theory and Deep Learning on Graphs](http://ikee.lib.auth.gr/record/294952/files/GRI-2017-20536.pdf) (2017)
>* [arXiv: Yann LeCun] [Graph Signal Processing: Overview, Challenges and Applications](https://arxiv.org/pdf/1712.00468.pdf) (Mar 2018)
>* [arXiv] [Spectral Domain Sampling of Graph Signals](https://arxiv.org/pdf/1706.05147.pdf) (May 2018)
>* [arXiv] [Graph Spectral Regularization for Neural Network Interpretability](https://arxiv.org/pdf/1810.00424.pdf) (Sep 2018)
>* [arXiv] [Weighted Spectral Embedding of Graphs](https://arxiv.org/pdf/1809.11115.pdf) (Sep 2018)  [[code](https://github.com/tbonald/spectral_embedding)]
>* [arXiv] [A Note on Spectral Clustering and SVD of Graph Data](https://arxiv.org/pdf/1809.11029.pdf)
>* [arXiv] [Graph Convolutional Neural Networks via Scattering](https://arxiv.org/pdf/1804.00099.pdf) (Mar 2018)
>* [arXiv] [Graph Generation via Scattering](https://arxiv.org/pdf/1809.10851.pdf) (Sep 2018)  [<a href="/2018/08/08/biokdd-review.html#1809.10851">discussed here</a>]
>* [arXiv] [Hypergraph Neural Networks](https://arxiv.org/pdf/1809.09401.pdf) (Sep 2018)
[victoria@victoria Linux]$
-->

EXECUTION:

REFERENCES: