File size: 4,376 Bytes
e1519c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | Dataframe with extracted metadata (table below with details) and full text of the collection for analysis : **size 489M**
| **Column name** | **Description** |
| :---------------: | :---------------------------: |
| `acl_id` | unique ACL id |
| `abstract` | abstract extracted by GROBID |
| `full_text` | full text extracted by GROBID |
| `corpus_paper_id` | Semantic Scholar ID |
| `pdf_hash` | sha1 hash of the pdf |
| `numcitedby` | number of citations from S2 |
| `url` | link of publication |
| `publisher` | - |
| `address` | Address of conference |
| `year` | - |
| `month` | - |
| `booktitle` | - |
| `author` | list of authors |
| `title` | title of paper |
| `pages` | - |
| `doi` | - |
| `number` | - |
| `volume` | - |
| `journal` | - |
| `editor` | - |
| `isbn` | - |
```python
>>> import pandas as pd
>>> df = pd.read_parquet('acl-publication-info.74k.parquet')
>>> df
acl_id abstract full_text corpus_paper_id pdf_hash ... number volume journal editor isbn
0 O02-2002 There is a need to measure word similarity whe... There is a need to measure word similarity whe... 18022704 0b09178ac8d17a92f16140365363d8df88c757d0 ... None None None None None
1 L02-1310 8220988 8d5e31610bc82c2abc86bc20ceba684c97e66024 ... None None None None None
2 R13-1042 Thread disentanglement is the task of separati... Thread disentanglement is the task of separati... 16703040 3eb736b17a5acb583b9a9bd99837427753632cdb ... None None None None None
3 W05-0819 In this paper, we describe a word alignment al... In this paper, we describe a word alignment al... 1215281 b20450f67116e59d1348fc472cfc09f96e348f55 ... None None None None None
4 L02-1309 18078432 011e943b64a78dadc3440674419821ee080f0de3 ... None None None None None
... ... ... ... ... ... ... ... ... ... ... ...
73280 P99-1002 This paper describes recent progress and the a... This paper describes recent progress and the a... 715160 ab17a01f142124744c6ae425f8a23011366ec3ee ... None None None None None
73281 P00-1009 We present an LFG-DOP parser which uses fragme... We present an LFG-DOP parser which uses fragme... 1356246 ad005b3fd0c867667118482227e31d9378229751 ... None None None None None
73282 P99-1056 The processes through which readers evoke ment... The processes through which readers evoke ment... 7277828 924cf7a4836ebfc20ee094c30e61b949be049fb6 ... None None None None None
73283 P99-1051 This paper examines the extent to which verb d... This paper examines the extent to which verb d... 1829043 6b1f6f28ee36de69e8afac39461ee1158cd4d49a ... None None None None None
73284 P00-1013 Spoken dialogue managers have benefited from u... Spoken dialogue managers have benefited from u... 10903652 483c818c09e39d9da47103fbf2da8aaa7acacf01 ... None None None None None
[73285 rows x 21 columns]
```
We are hoping that this corpus can be helpful for analysis relevant to the ACL community.
**Please cite/star 🌟 this page if you use this corpus**
## Citing the ACL Anthology Corpus
If you use this corpus in your research please use the following BibTeX entry:
@Misc{acl_anthology_corpus,
author = {Shaurya Rohatgi},
title = {ACL Anthology Corpus with Full Text},
howpublished = {Github},
year = {2022},
url = {https://github.com/shauryr/ACL-anthology-corpus}
}
## Acknowledgements
We thank Semantic Scholar for providing access to the citation related data in this corpus.
## License
ACL anthology corpus is released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). By using this corpus, you are agreeing to its usage terms.
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