https://github.com/kermitt2/entity-fishing
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update for grobid version 0.8.0
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Readme.md
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# entity-fishing
*entity-fishing* performs the following tasks for 15 different languages:
* general entity recognition and disambiguation against Wikidata in a raw text or partially-annotated text segment,
* general entity recognition and disambiguation against Wikidata at document level, in particular for a PDF with layout positioning and structure-aware annotations,
* search query disambiguation (the _short text_ mode) - below disambiguation of the search query "concrete pump sensor" in the service test console,
* weighted term vector disambiguation (a term being a phrase),
* interactive disambiguation in text editing mode (experimental).
# Documentation
[Presentation of entity-fishing at WikiDataCon 2017](https://grobid.s3.amazonaws.com/presentations/29-10-2017.pdf) for some design, implementation descriptions, and some evaluations.
The documentation of *entity-fishing* is available [here](http://nerd.readthedocs.io).
*entity-fishing* uses a query DSL (entity disambiguation specific query language) documented [here](https://nerd.readthedocs.io/en/latest/restAPI.html).
# Demo
For testing purposes, a public entity-fishing demo server (current version) is available at the following address: [https://cloud.science-miner.com/nerd](https://cloud.science-miner.com/nerd)
The query DSL and Web services are documented [here](https://nerd.readthedocs.io/en/latest/restAPI.html).
_Warning_: Some quota and query limitation apply to the demo server! Please be courteous and do not overload the demo server.
A [docker image](https://nerd.readthedocs.io/en/latest/docker.html) is available to help you deploying your own server.
# Benchmarks
Evaluations above correspond to the "overall unnormalized accuracy" scenario in [BLINK](https://github.com/facebookresearch/BLINK#benchmarking-blink) and are limited to Named Entities. entity-fishing performs at 0.765 F-score, as compared to 0.8027 for BLINK, a fine-tuned BERT architectures. *entity-fishing* surpasses BLINK for the dataset AQUAINT, 0.891 vs. 0.8588, and MSNBC, 0.867 vs. 0.8509, despite being considerably faster and lighter than BLINK (see below).
See the [evaluation documentation](https://nerd.readthedocs.io/en/latest/evaluation.html) and [Presentation of entity-fishing at WikiDataCon 2017](https://grobid.s3.amazonaws.com/presentations/29-10-2017.pdf) for more details.
*entity-fishing* has been designed to be particularly fast for a full scale Wikidata-based entity disambiguation tool (in particular not limited to Named Entities). On a single server, depending on the concurrency, it is possible to process from 1.000-1.500 tokens per seconds with concurrency 1 to 5.000 tokens per second for instance with 6 concurrent requests in English (and up to 2 times faster for other languages).
# Some use cases
Some example of *entity-fishing* usages:
* A [spaCy wrapper](https://spacy.io/universe/project/spacyfishing) for entity-fishing is available since 2022, see the [project repo](https://github.com/Lucaterre/spacyfishing), thanks to Lucas Terriel. Note that the wrapper is disambiguating Named Entities recognized by NER spaCy models, while *entity-fishing* has also been designed to cover general wikidata entities based on all Wikipedia entries, anchors and redirections.
* Tanti Kristanti from [Inria Paris](https://www.inria.fr) used off the shelf version of *entity-fishing* in the [CLEF HIPE 2020 competition shared task](http://ceur-ws.org/Vol-2696/paper_266.pdf), ranking first at the Entity Linking task for English and second best for French, in F1-score.
* [Kairntech](https://kairntech.com) has integrated *entity-fishing* on their commercial platform [Sherpa](https://aclanthology.org/2020.iwltp-1.9.pdf) to support analysis and enrichment of textual content, since 2020.
* [SEALK](https://sealk.co), which is commercializing a M&A industry recommendation system, scaled *entity-fishing* to more than 1 million fulltext news documents in 2020.
* In 2018, *entity-fishing* has been deployed in the DARIAH-EU and Huma-Num infrastructure in the context of the [OPERAS HIRMEOS EU project](https://www.hirmeos.eu).
If you are using *entity-fishing* and found it useful, we are happy to mention you in this section !
# Current version
*entity-fishing* is a **work-in-progress** side project! Latest release version is `0.0.6`.
This version supports 15 languages: English, French, German, Italian, Spanish, Arabic, Mandarin, Russian, Japanese, Portuguese, Farsi, Ukrainian, Swedish, Bengali and Hindi with an in-house Named Entity Recognizer available for English and French. For this version, the available knowledge base includes around 96 million entities from Wikidata - but you can create your own fresh knowledge base with the [GRISP](https://github.com/kermitt2/grisp) utility.
**Runtime**: version `0.0.6` 2022, on a local machine (Intel Haswel i7-4790K CPU 4.00GHz - 8 cores - 16GB - SSD, 2015) for **English** (other languages are up to 50% faster - these runtimes do not include a 15s-30s initial server launch/start-up and are obtained with an empty cache and without LMDB pages pre-loaded in RAM memory).
* 800 pubmed abstracts (172 787 tokens) processed in 129s with 1 client (1339 tokens/s)
* 4800 pubmed abstracts (1 036 722 tokens) processed in 168s with 6 concurrent clients (6171 tokens/s)
* 136 PDF (3443 pages, 1 422 943 tokens) processed in 760s with 1 client (4.5 pages/s, 1872 tokens/s)
* 816 PDF (20658 pages, 8 537 658 tokens) processed in 1133s with 6 concurrent clients (18.2 pages/s, 7535 tokens/s)
**Accuracy**: F1-score for disambiguation only between 76.5 and 89.1 on standard datasets (ACE2004, AIDA-CONLL-testb, AQUAINT, MSNBC) - to be improved in the future versions.
The knowledge base contains more than 1.5 billion objects, not far from 15 millions word and entity embeddings, however *entity-fishing* will work with 3-4 GB RAM memory after a 15 second start-up for the server - but please use SSD!
## How to cite
If you want to cite this work, please refer to the present GitHub project, together with the [Software Heritage](https://www.softwareheritage.org/) project-level permanent identifier. For example, with BibTeX:
```bibtex
@misc{entity-fishing,
title = {entity-fishing},
howpublished = {\url{https://github.com/kermitt2/entity-fishing}},
publisher = {GitHub},
year = {2016--2023},
archivePrefix = {swh},
eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
}
```
## License and contact
Distributed under [Apache 2.0 license](http://www.apache.org/licenses/LICENSE-2.0). The dependencies used in the project are either themselves also distributed under Apache 2.0 license or distributed under a compatible license.
If you contribute to this project, you agree to share your contribution following these licenses.
Main author and contact: Patrice Lopez (<patrice.lopez@science-miner.com>)
*entity-fishing* has been created, developed and is maintained by [SCIENCE-MINER](http://science-miner.com/entity-disambiguation/). The development started in 2015, with the first Open Source public version available in 2016.
The project has received support from [Kairntech](https://kairntech.com) (2022) under a [RAPID](https://www.defense.gouv.fr/aid/deposez-votre-projet/rapid-regime-dappui-a-linnovation-duale) grant and contributions from [Inria](http://inria.fr) Paris (2017-2018).
