entity-fishing performs the following tasks:
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entity recognition and disambiguation against Wikidata in a raw text or partially-annotated text segment,
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entity recognition and disambiguation against Wikidata at document level, in particular for a PDF with layout positioning and structure-aware annotations,
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search query disambiguation (the short text mode) - below disambiguation of the search query "concrete pump sensor" in the service test console,
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weighted term vector disambiguation (a term being a phrase),
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interactive disambiguation in text editing mode (experimental).
Presentation of entity-fishing at WikiDataCon 2017 for some design, implementation descriptions, and some evaluations.
The documentation of entity-fishing is available here.
For testing purposes, a public entity-fishing demo server is available at the following address: https://cloud.science-miner.com/nerd
The query DSL and Web services are documented here.
Warning: Some quota and query limitation apply to the demo server! Please be courteous and do not overload the demo server.
Evaluations above correspond to the "overall unnormalized accuracy" scenario in BLINK (entity-fishing performs at 0.765 F-score, as compared to 0.8027 for BLINK, a fine-tuned BERT architectures and surpasses this system in the AQUAINT, 0.8588 vs. 0.891, and MSNBC, 0.8509 vs 0.867, despite being considerably faster and lighter than BLINK, see below).
See the evaluation documentation and Presentation of entity-fishing at WikiDataCon 2017 for more details.
entity-fishing is a work-in-progress! Latest release version is 0.0.4.
This version supports English, French, German, Italian and Spanish, with an in-house Named Entity Recognizer for English and French. For this version, the knowledge base includes around 87 million entities and 1.1 billion statements from Wikidata.
Runtime: on local machine (Intel Haswel i7-4790K CPU 4.00GHz - 8 cores - 16GB - SSD)
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800 pubmed abstracts (172 787 tokens) processed in 126s with 1 client (1371 tokens/s)
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4800 pubmed abstracts (1 036 722 tokens) processed in 216s with 6 concurrent clients (4800 tokens/s)
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136 PDF (3443 pages, 1 422 943 tokens) processed in 1284s with 1 client (2.6 pages/s, 1108.2 tokens/s)
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816 PDF (20658 pages, 8 537 658 tokens) processed in 2094s with 6 concurrent clients (9.86 pages/s, 4077 tokens/s)
Accuracy: f-score for disambiguation only between 76.5 and 89.1 on standard datasets (ACE2004, AIDA-CONLL-testb, AQUAINT, MSNBC) - to be improved in the next 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!
If you want to cite this work, please refer to the present GitHub project, together with the Software Heritage project-level permanent identifier. For example, with BibTeX:
@misc{entity-fishing,
title = {entity-fishing},
howpublished = {\url{https://github.com/kermitt2/entity-fishing}},
publisher = {GitHub},
year = {2016--2020},
archivePrefix = {swh},
eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
}Distributed under Apache 2.0 license. The dependencies used in the project are either themselves also distributed under Apache 2.0 license or distributed under a compatible license.
Main author and contact: Patrice Lopez (patrice.lopez@science-miner.com)
entity-fishing is developed and maintained by SCIENCE-MINER (since 2015, first Open Source public version in 2016), with contributions of Inria Paris (2017-2018).