diff options
| -rw-r--r-- | README.md | 84 | ||||
| -rw-r--r-- | s2-dump-ids.py | 2 | ||||
| -rw-r--r-- | s2-dump-pdf-urls.py | 37 | ||||
| -rw-r--r-- | samples/s2-orc-paper.json | 131 | ||||
| -rw-r--r-- | samples/s2-papers-api.json | 2855 | ||||
| -rw-r--r-- | samples/s2-search-api.json | 270 |
6 files changed, 3358 insertions, 21 deletions
@@ -3,29 +3,73 @@ ## installation ``` +conda create -n megapixels python=3.7 pip install urllib3 -pip install bs4 -pip install http +pip install requests +pip install simplejson +pip install click +pip install pdfminer.six +pip install csvtool +npm install ``` -## ascii +## workflow ``` -xxxxdddd5xxdddd5555vvvv~xxxxdddd55vvvv7777~~~~xxxxdddddd5555vvvv7777 -xxxxdddd5xxdddd55v~xxxxddddddddd55vvvv7777~~~~xxxxdddd5555v5vvvv7777 -xxxxdddd555vvvv77~~xxxxdddddddd55vvvv77777~~~xxxxddd55555v5vvvv77777 -aaaaxddd5555vvvv77xxxxddddddddd55vvvvv77777~~xxxxddd55555v5vvvvddddd -aaaaayyMMMMMqqqqddeeeexxxxxxdddd55vvvv77777~~xxaayyyyMMMMMqMqqqvdddd -aaaaayyMMMMMqqqqqddeeeaaaaaayyyyMMqqqqqddddeeaaaayyyyMMMMMqMqqqqdddd -ccccaDD%%%%%##qqqddeeaaaaaayyyyMMqqqqddddeeeeaaaayyyyMMMMMqMqqqq@@@@ -ccccc%%%###qqqd#@@eeeaaaaaayyyyMMqqqqddddeeeeacccDDDD%%%%%#%###q@@@@ -cccx%%%%###i##@#@eeeeaccccccDyyyM%##qddd@@eeeeccccDDyMMMMqMqqqq@@@@@ -llcccDDDD%%%%###@eeeecccccccDDDD%%####@@@@eeeecccDDD%%%%#%###q@&&&&& -lllllSDDD%%%%###@llllcccccccDDDD%%####@@@@eeeecclSSD%%%%#%####@&&&&& -llllSSSSQQ%%%##&&&lllllcclllSDDD%%####@@@@eellcllSSD%%%%#%####&&&&&& -aallSSSSQQQQQ%%&&&llllllllllSSSD%Q%%##@@&&llllllllSSQQQQ%Q%%%#&&rrrr -aaaaiiiiVVQQQ%%drrrrrrrrllllSSSSQQ%%%%&&&&llllllllSSSQQQQ%Q%%%%&rrrr -aaaaiiiiVVVVVdddrrrrrrrraaaaiSSSQVdd%%&&rrrrrraaaaiiSQQQQ%Q%%%%rrrrr -xxxxdddd55VVVddv7777~~~~aaaaiiiiVVddddrrrrrrrraaaaiiiiVVVVdVdddd7777 -xxxxdddd5555vvvv7777~~~~xxxxdiiiV555vvvv7777~~~~xxxxdddd5555vvvv7777 +Paper in spreadsheet -> paper_name + -> S2 Search API -> paper_id + -> S2 Paper API -> citations + -> S2 Dataset -> full records with PDF URLs, authors, more citations + -> wget -> .pdf files + -> pdfminer.six -> pdf text + -> Stanford NER -> named entities (organizations) + -> Geocoding service -> lat/lngs ``` + +To begin, export `datasets/citations.csv` from the Google doc. + +## Extracting data from S2 / ORC + +The Open Research Corpus (ORC) is produced by the Allen Institute / Semantic Scholar (S2) / arXiv people. It may be downloaded here: + +http://labs.semanticscholar.org/corpus/ + +### s2-search.py + +Loads titles from citations file and queries the S2 search API to get paper IDs. + +### s2-papers.py + +Uses the paper IDs from the search entries to query the S2 papers API to get first-degree citations, authors, etc. + +### s2-dump-ids.py + +Extract all the paper IDs and citation IDs from the queried papers. + +### s2-extract-papers.py + +Extracts papers from the ORC dataset which have been queried from the API. + +### s2-dump-pdf-urls.py + +Dump PDF urls (and also IEEE urls etc) to pdfs.json, ieee.json, .... + +### s2-fetch-pdfs.py + +Fetch the files listed in pdfs.json and process them. + +### s2-fetch-ieee.py + +Fetch the files listed in ieee.json and process them. + +### s2-extract-first-page.py + +pdfminer the first page from the dumped PDFs. + +### s2-extract-entities.py + +Extract named entities from the mined text. + +### s2-geocode.py + +Geocode known entities from the database. diff --git a/s2-dump-ids.py b/s2-dump-ids.py index 3218738a..8ecb0b6a 100644 --- a/s2-dump-ids.py +++ b/s2-dump-ids.py @@ -26,7 +26,7 @@ def process_paper(fn, ids): ids[cite['paperId']] = True def paper_path(paper_id): - return '{}/{}/{}'.format(DATA_DIR, paper_id[0:3], paper_id) + return '{}/{}/{}'.format(DATA_DIR, paper_id[0:2], paper_id) if __name__ == '__main__': s2_dump_ids() diff --git a/s2-dump-pdf-urls.py b/s2-dump-pdf-urls.py new file mode 100644 index 00000000..111ed830 --- /dev/null +++ b/s2-dump-pdf-urls.py @@ -0,0 +1,37 @@ +import os +import glob +import simplejson as json +import click +from util import * + +PAPER_JSON_DIR = 'datasets/s2/db_papers' + +@click.command() +def s2_dump_pdf_urls(): + # loop over all the papers in db_papers + # get all the PDF urls, pick the best one + # store it and the paper id + # another script will fetch the urls from this process + ids = {} + for fn in glob.iglob('{}/**/paper.json'.format(PAPER_JSON_DIR), recursive=True): + process_paper(fn, ids) + id_list = list(ids.keys()) + print("Wrote {} ids".format(len(id_list))) + write_csv('pdf_list.csv', id_list) + + for line in lines: + label = line[0] + title = re.sub(r'[^-0-9a-zA-Z ]+', '', line[1]) + entry_fn = './datasets/s2/entries/{}.json'.format(title) + if not os.path.exists(entry_fn): + results = s2.search(title) + write_json(dump_fn, results) + if len(results['results']) == 0: + print("No results for {}".format(title)) + else: + print(title) + write_json(entry_fn, results['results'][0]) + time.sleep(random.randint(10, 20)) + +if __name__ == '__main__': + s2_dump_pdf_urls() diff --git a/samples/s2-orc-paper.json b/samples/s2-orc-paper.json new file mode 100644 index 00000000..1fb29126 --- /dev/null +++ b/samples/s2-orc-paper.json @@ -0,0 +1,131 @@ +{ + "entities": [ + "Database", + "Experiment", + "Facial recognition system", + "Optimization problem", + "Program optimization", + "Simultaneous localization and mapping", + "Taxicab geometry", + "The Matrix", + "Video game localization" + ], + "journalVolume": "", + "journalPages": "3871-3879", + "pmid": "", + "year": 2015, + "outCitations": [ + "bd5accc96a772f2bbb7c64af0aa0b600b0b8594b", + "0e3fcfe63b7b6620e3c47e9751fe3456e85cc52f", + "023f6fc69fe1f6498e35dbf85932ecb549d36ca4", + "54aafe33c4a32fb5875d04fd4a6e6da50920c263", + "9f87e3212ab1d89c5d46924c925d6bb1da02f92b", + "0f0fcf041559703998abf310e56f8a2f90ee6f21", + "53b919c994b3658cca8178e455681ca244a7afad", + "084bd02d171e36458f108f07265386f22b34a1ae", + "14ce7635ff18318e7094417d0f92acbec6669f1c", + "e42998bbebddeeb4b2bedf5da23fa5c4efc976fa", + "0cb48f543c4bf329a16c0408c4d2c198679a6057", + "0de91641f37b0a81a892e4c914b46d05d33fd36e", + "3f204a413d9c8c16f146c306c8d96b91839fed0c", + "3393459600368be2c4c9878a3f65a57dcc0c2cfa", + "1824b1ccace464ba275ccc86619feaa89018c0ad", + "70eeacf9f86ba08fceb3dd703cf015016dac1930", + "5040f7f261872a30eec88788f98326395a44db03", + "95f12d27c3b4914e0668a268360948bce92f7db3", + "2ee56d6c29072abd576ef16bcb26360c42caaf5f", + "370b5757a5379b15e30d619e4d3fb9e8e13f3256", + "0296fc4d042ca8657a7d9dd02df7eb7c0a0017ad", + "129388116bc3229546a84b9bc3d11fdac8b93201", + "63f9f3f0e1daede934d6dde1a84fb7994f8929f0", + "03f98c175b4230960ac347b1100fbfc10c100d0c", + "32d6ec2810b52d1df128be51464bc43b53702232", + "1a1a60fd4dc88a14c016b95789385801c6b80574", + "800683c891b9c5934246ce2931d1d28e0a364fbf", + "0e986f51fe45b00633de9fd0c94d082d2be51406", + "0dccc881cb9b474186a01fd60eb3a3e061fa6546", + "f731b6745d829241941307c3ebf163e90e200318", + "07119bc66e256f88b7436e62a4ac3384365e4e9b", + "0a5bf19c1b297713a3267bc670b252f9e51c2b78", + "013909077ad843eb6df7a3e8e290cfd5575999d2", + "177bc509dd0c7b8d388bb47403f28d6228c14b5c", + "b2d9877443ec7da2490027ccc932468f05c7bf85", + "b960984881665f692764069015764d25974c8d1b", + "4998462014c907c519ae801af39cf58a4c538bc9", + "5d1c4e93e32ee686234c5aae7f38025523993c8c", + "64d5772f44efe32eb24c9968a3085bc0786bfca7", + "1c08824da1383051fba69384a3edf135ba58e7fd", + "b5a3cc76ceee9489e66c5929c66aec1cf5210241", + "4068574b8678a117d9a434360e9c12fe6232dae0", + "830e5b1043227fe189b3f93619ef4c58868758a7", + "044d9a8c61383312cdafbcc44b9d00d650b21c70", + "3957b51b44f8727fe008162ea8a142a8c7917dea", + "55b4b1168c734eeb42882082bd131206dbfedd5b", + "140438a77a771a8fb656b39a78ff488066eb6b50", + "31a38fd2d9d4f34d2b54318021209fe5565b8f7f" + ], + "s2Url": "https://semanticscholar.org/paper/788a7b59ea72e23ef4f86dc9abb4450efefeca41", + "s2PdfUrl": "", + "id": "788a7b59ea72e23ef4f86dc9abb4450efefeca41", + "authors": [ + { + "name": "Christos Sagonas", + "ids": [ + "3320415" + ] + }, + { + "name": "Yannis Panagakis", + "ids": [ + "1780393" + ] + }, + { + "name": "Stefanos Zafeiriou", + "ids": [ + "1776444" + ] + }, + { + "name": "Maja Pantic", + "ids": [ + "1694605" + ] + } + ], + "journalName": "2015 IEEE International Conference on Computer Vision (ICCV)", + "paperAbstract": "Recently, it has been shown that excellent resultscan be achieved in both facial landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D facial data. In this paper, we propose a novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only. By observing that the frontal facial image is the one having the minimum rank of all different poses, an appropriate model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix l1 norm is solved. The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions. The relevant experiments have been conducted on 8 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.", + "inCitations": [ + "0e2365254f7833a1a902a6439f42ece6e5326332", + "368d59cf1733af511ed8abbcbeb4fb47afd4da1c", + "0db8e6eb861ed9a70305c1839eaef34f2c85bbaf", + "fd46586ff7d1cdedc184051268bb07a6630bc72e", + "239401dc0dc4c64aa741a75f7324768ef7557889", + "ef21738c40f78fd5834cf5910fc58f45f88cf698", + "566c104c9a494ae385ab253e3c69864f69d53b52", + "5cf7ac83f2b63f2c090757bcf08df4bd2dd63b81", + "c62c910264658709e9bf0e769e011e7944c45c90", + "b730f01e47151ccfaf568285acf21374efc53f88", + "0822cf1f041b2e572a038667474e79acab264ebf", + "566a2ede36a6493010ea42a7df49916739e00c9d", + "b1a3b19700b8738b4510eecf78a35ff38406df22", + "6e9a8a34ab5b7cdc12ea52d94e3462225af2c32c", + "a06b6d30e2b31dc600f622ab15afe5e2929581a7", + "52dfa36d756a46cf11e6537463add3bdd9bb4011" + ], + "pdfUrls": [ + "http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf", + "http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.441", + "http://openaccess.thecvf.com/content_iccv_2015/papers/Sagonas_Robust_Statistical_Face_ICCV_2015_paper.pdf", + "http://ibug.doc.ic.ac.uk/media/uploads/documents/robust_frontalization.pdf", + "http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Sagonas_Robust_Statistical_Face_ICCV_2015_paper.pdf", + "https://ibug.doc.ic.ac.uk/media/uploads/documents/robust_frontalization.pdf" + ], + "title": "Robust Statistical Face Frontalization", + "doi": "10.1109/ICCV.2015.441", + "sources": [ + "DBLP" + ], + "doiUrl": "https://doi.org/10.1109/ICCV.2015.441", + "venue": "2015 IEEE International Conference on Computer Vision (ICCV)" +}
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