https://github.com/kermitt2/grobid-ner
Tip revision: cd02a2ab0696d8f0fded5c489f5a2dc1ff7d42c2 authored by lopez on 26 November 2023, 20:02:27 UTC
Merge branch 'master' of github.com:kermitt2/grobid-ner
Merge branch 'master' of github.com:kermitt2/grobid-ner
Tip revision: cd02a2a
Readme.md
# grobid-ner
[](http://grobid-ner.readthedocs.io/en/latest/)
[](http://www.apache.org/licenses/LICENSE-2.0.html)
[](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/kermitt2/grobid-ner)
## Purpose
__grobid-ner__ is a Named-Entity Recogniser based on [GROBID](https://github.com/kermitt2/grobid), a text mining tool. The installation of GROBID is necessary.
For a description of the NER, installation, usage and other technical features, see the [documentation](http://grobid-ner.readthedocs.io/en/latest/).
This project has been developed to explore NER with CRF. It allows in particular reliable and reproducible comparisons with other approaches. For comparisons with Deep Learning architectures used for state-of-the-art sequence labelling, see the project [DeLFT](https://github.com/kermitt2/delft).
__grobid-ner__ is originally more specifically dedicated to the support of disambiguation and resolution of the entities against knowledge bases such as Wikidata. The project comes with a NER model for English with 27 named entity classes and its corresponding dataset, all CC-BY. The high number of classes is motivated to help further entity disambiguation and to bring more entities than the usual datasets.
## Benchmarking
### 27-class English dataset
```
Summary results:
Average over 10 folds:
label accuracy precision recall f1 support
ANIMAL 79.92 45 35 38.33 12
ARTIFACT 89.73 45 31.67 34.67 25
AWARD 79.86 33.33 25 25 11
BUSINESS 99.35 57.5 37.58 43.83 61
CONCEPT 99.5 59.5 42.01 46.44 45
CONCEPTUAL 89.85 61.67 41.67 48.05 19
CREATION 19.98 0 0 0 2
EVENT 98.19 56.95 45.73 50.25 175
IDENTIFIER 69.92 41.67 41.67 40 12
INSTALLATION 59.9 30 21.67 24.67 10
INSTITUTION 29.97 0 0 0 3
LOCATION 97.53 89.37 93.14 91.18 1126
MEASURE 98.5 89.77 91.18 90.45 650
MEDIA 89.77 18.33 12.5 13 15
NATIONAL 98.85 83.16 89.59 86.02 337
ORGANISATION 98.64 72.46 66.96 69.14 196
PERIOD 99.17 92.3 89.82 90.92 384
PERSON 98.51 87.98 89.81 88.8 539
PERSON_TYPE 99.71 89.13 62.51 71.44 55
SPORT_TEAM 99.15 77.95 68.46 71.08 133
TITLE 99.5 66.33 48.25 54.97 62
UNKNOWN 39.95 0 0 0 3
WEBSITE 40 40 40 40 4
all 99.22 85.55 83.11 84.31
===== Instance-level results =====
Total expected instances: 109.1
Correct instances: 65.9
Instance-level recall: 60.4
Worst fold
===== Field-level results =====
label accuracy precision recall f1 support
ANIMAL 99.89 0 0 0 1
ARTIFACT 99.89 50 100 66.67 1
AWARD 99.89 100 50 66.67 2
BUSINESS 99.34 33.33 20 25 5
CONCEPT 99.34 0 0 0 4
CONCEPTUAL 99.67 50 33.33 40 3
CREATION 99.89 0 0 0 1
EVENT 98.46 40 33.33 36.36 12
IDENTIFIER 99.78 0 0 0 2
INSTALLATION 99.78 0 0 0 1
LOCATION 97.35 90 91.41 90.7 128
MEASURE 98.68 88.06 93.65 90.77 63
MEDIA 99.89 0 0 0 1
NATIONAL 98.46 83.64 90.2 86.79 51
ORGANISATION 97.46 43.75 33.33 37.84 21
PERIOD 98.57 85.71 78.95 82.19 38
PERSON 98.68 89.66 89.66 89.66 58
PERSON_TYPE 99.78 100 33.33 50 3
SPORT_TEAM 99.56 81.25 92.86 86.67 14
TITLE 99.01 33.33 12.5 18.18 8
UNKNOWN 99.89 0 0 0 1
all (micro avg.) 99.2 83.08 79.9 81.46 418
all (macro avg.) 99.2 46.13 40.6 41.31 418
===== Instance-level results =====
Total expected instances: 109
Correct instances: 60
Instance-level recall: 55.05
Best fold:
===== Field-level results =====
label accuracy precision recall f1 support
ARTIFACT 99.86 100 50 66.67 2
AWARD 99.58 0 0 0 2
BUSINESS 99.86 100 80 88.89 5
CONCEPT 99.72 66.67 66.67 66.67 3
CONCEPTUAL 99.86 0 0 0 1
CREATION 99.86 0 0 0 1
EVENT 98.33 53.85 53.85 53.85 13
IDENTIFIER 99.86 50 100 66.67 1
INSTALLATION 99.86 0 0 0 1
LOCATION 98.06 93.1 91.01 92.05 89
MEASURE 98.47 92.11 93.33 92.72 75
MEDIA 99.58 33.33 50 40 2
NATIONAL 99.03 80 90.91 85.11 22
ORGANISATION 99.31 80 85.71 82.76 14
PERIOD 98.89 96.97 82.05 88.89 39
PERSON 98.33 84.62 96.49 90.16 57
PERSON_TYPE 99.86 100 50 66.67 2
SPORT_TEAM 99.31 100 58.33 73.68 12
TITLE 99.72 100 66.67 80 6
all (micro avg.) 99.33 87.65 85.88 86.75 347
all (macro avg.) 99.33 64.77 58.69 59.72 347
===== Instance-level results =====
Total expected instances: 109
Correct instances: 77
Instance-level recall: 70.64
```
Runtime __single thread__: 18,599 tokens per second. Tokens are defined by GROBID default tokenizers. Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz, 16GB RAM.
### Corpus LeMonde (FTB, French)
```
Average over 10 folds:
===== Field-level results =====
label accuracy precision recall f1 support
ARTIFACT 99.94 95.56 81.56 86.63 67
BUSINESS 95.71 84.8 83.27 84 3537
INSTITUTION 99.72 88.94 76 81.69 212
LOCATION 97.95 92.55 93.33 92.93 3778
ORGANISATION 96.77 81.75 73.97 77.62 1979
PERSON 98.83 93.32 91.54 92.42 2044
all (micro avg.) 98.15 88.54 86.28 87.39 1161.7
===== Instance-level results =====
Total expected instances: 1262.5
Correct instances: 1110.6
Instance-level recall: 87.97
Worst fold
===== Field-level results =====
label accuracy precision recall f1 support
ARTIFACT 99.96 100 83.33 90.91 6
BUSINESS 94.82 81.42 79.54 80.47 347
INSTITUTION 99.65 84.21 72.73 78.05 22
LOCATION 97.88 90.51 94.35 92.39 354
ORGANISATION 96.79 84.53 73.56 78.66 208
PERSON 98.61 91.5 90.59 91.04 202
all (micro avg.) 97.95 86.88 84.9 85.88 1139
all (macro avg.) 97.95 88.7 82.35 85.25 1139
===== Instance-level results =====
Total expected instances: 1262
Correct instances: 1107
Instance-level recall: 87.72
Best fold:
===== Field-level results =====
label accuracy precision recall f1 support
ARTIFACT 99.93 100 80 88.89 10
BUSINESS 96.03 87.16 84.17 85.64 379
INSTITUTION 99.7 84.21 76.19 80 21
LOCATION 98.03 93.56 93.33 93.45 405
ORGANISATION 97.18 81.4 76.09 78.65 184
PERSON 99.07 94.47 93.07 93.77 202
all (micro avg.) 98.32 89.81 87.34 88.56 1201
all (macro avg.) 98.32 90.13 83.81 86.73 1201
===== Instance-level results =====
Total expected instances: 1262
Correct instances: 1099
Instance-level recall: 87.08
```
Runtime __single thread__: 88,451 tokens labeled in 1228 ms, 72,028 tokens per second. Tokens are defined by GROBID default tokenizers. This is Wapiti CRF labeling only, without the additional post-processing. Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz, 16GB RAM.
### CoNLL-2003 (English)
We use report results with the official CoNLL eval script:
```
processed 52236 tokens with 5628 phrases; found: 5612 phrases; correct: 4805.
accuracy: 96.86%; precision: 85.62%; recall: 85.38%; FB1: 85.50
LOCATION: precision: 88.63%; recall: 90.07%; FB1: 89.35 1689
ORGANISATION: precision: 81.23%; recall: 79.95%; FB1: 80.58 1630
PERSON: precision: 90.47%; recall: 91.03%; FB1: 90.75 1627
UNKNOWN: precision: 76.88%; recall: 73.88%; FB1: 75.35 666
```
Runtime __single thread__: 29,781 tokens per second. Tokens are defined by GROBID default tokenizers. Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz, 16GB RAM.
## License
Grobid and grobid-ner are distributed under [Apache 2.0 license](http://www.apache.org/licenses/LICENSE-2.0).
Author and contact: Patrice Lopez (<patrice.lopez@science-miner.com>)
