taxonerd 1.1.1

Deep neural models for taxonomic entity recognition

Looking for taxon mentions in text? Ask TaxoNERD

• Features
• Installation
• Usage

Features

TaxoNERD is a domain-specific tool for recognizing taxon mentions in the biodiversity literature.

• You can choose between two models : en_ner_eco_md uses spaCy's standard Tok2Vec with word vectors for speed, while en_ner_eco_biobert uses a Transformer-based pretrained language model (dmis-lab/biobert-v1.1) for accuracy.
• TaxoNERD finds scientific names, common names and user-defined abbreviations
• TaxoNERD can link taxon mentions to entities in a reference taxonomy (NCBI Taxonomy, GBIF Backbone and TAXREF at the moment, more to come)
• TaxoNERD is fast (once the model is loaded), and can run on CPU or GPU
• Entity linking does not need an internet connection, but may require a lot of RAM depending on the size of the taxonomy (e.g. GBIF Backbone -> ~12.5Gb)
• Thanks to textract, TaxoNERD can extract taxon mentions from (almost) any document (including txt, pdf, csv, xls, jpg, png, and many other formats)
• Available as a command-line tool and a python module

Installation

TaxoNERD is only available for Python 3.8+

$ pip install taxonerd
$ pip install https://github.com/nleguillarme/taxonerd/releases/download/v1.1.1/en_ner_eco_md-1.0.0.tar.gz
$ pip install https://github.com/nleguillarme/taxonerd/releases/download/v1.1.1/en_ner_eco_biobert-1.0.0.tar.gz

Entity linker files are downloaded and cached the first time the linker is used. This may take some time, but it should only be done once. Currently (v1.1.1), there are 3 supported linkers:

• gbif_backbone: Links to GBIF Backbone Taxonomy (2019-09-06) (~9.5M names for ~3.5M taxa).
• taxref: Links to TAXREF (v13) (~1.2M names for ~267k taxa).
• ncbi_taxonomy: Links to The NCBI Taxonomy (~3.4M names).

Usage

Use as command-line tool

Usage: taxonerd ask [OPTIONS] [INPUT_TEXT]

Options:
  --focus-on TEXT        Focus on either speed or accuracy
  -i, --input-dir TEXT   Input directory
  -o, --output-dir TEXT  Output directory
  -f, --filename TEXT    Input text file
  -a, --with-abbrev      Add abbreviation detector to the pipeline
  -l, --link-to TEXT     Add entity linker to the pipeline
  -t, --thresh FLOAT     Similarity threshold for entity candidates (default = 0.7)

  --prefer-gpu           Use GPU if available
  -v, --verbose          Verbose mode
  --help                 Show this message and exit.

Examples

Taxonomic NER from the terminal

$ taxonerd ask --focus-on accuracy "Brown bears (Ursus arctos), which are widely distributed throughout the northern hemisphere, are recognised as opportunistic omnivores"
T0	LIVB 0 11	Brown bears
T1	LIVB 13 25	Ursus arctos

Taxonomic NER with entity linking from the terminal

$ taxonerd ask --focus-on accuracy -l gbif_backbone "Brown bears (Ursus arctos), which are widely distributed throughout the northern hemisphere, are recognised as opportunistic omnivores"
T0	LIVB 0 11	Brown bears	[('GBIF:2433433', 'Brown Bear', 0.8313919901847839)]
T1	LIVB 13 25	Ursus arctos	[('GBIF:2433433', 'Ursus arctos', 1.0)]

$ taxonerd ask --focus-on accuracy -l gbif_backbone -t 0.85 "Brown bears (Ursus arctos), which are widely distributed throughout the northern hemisphere, are recognised as opportunistic omnivores"
T0	LIVB 13 25	Ursus arctos	[('GBIF:2433433', 'Ursus arctos', 1.0)]

Taxonomic NER from a text file (with abbreviation detection)

$ taxonerd ask --focus-on accuracy --with-abbrev -f test_txt/sample_text1.txt
T0	LIVB 4 21	pinewood nematode
T1	LIVB 29 55	Bursaphelenchus xylophilus
T2	LIVB 83 100	pine wilt disease
T3	LIVB 180 188	Serratia
T4	LIVB 326 348	Serratia grimesii BXF1
T5	LIVB 424 428	BXF1
T6	LIVB 23 26	PWN
T7	LIVB 241 244	PWN
T8	LIVB 371 374	PWN
T9	LIVB 102 105	PWD
T10	LIVB 313 316	PWD

Taxonomic NER from a directory containing text files, with results written in the output directory

$ taxonerd ask --focus-on accuracy -i test_txt -o test_ann
$ ls test_ann/
sample_text1.ann  sample_text2.ann
$ cat test_ann/sample_text2.ann
T0	LIVB 700 711	Brown bears
T1	LIVB 713 725	Ursus arctos
T2	LIVB 1062 1073	brown bears
T3	LIVB 1161 1172	brown bears
T4	LIVB 1339 1350	brown bears
T5	LIVB 1555 1565	brown bear
T6	LIVB 1782 1793	brown bears
T7	LIVB 1863 1874	brown bears
T8	LIVB 1958 1969	brown bears
T9	LIVB 2026 2037	brown bears
T10	LIVB 2219 2230	brown bears
T11	LIVB 2392 2401	Sika deer
T12	LIVB 2403 2416	Cervus nippon
T13	LIVB 2555 2559	deer
T14	LIVB 2594 2604	brown bear
T15	LIVB 2798 2808	brown bear
T16	LIVB 3135 3141	salmon
T17	LIVB 3146 3150	deer
T18	LIVB 3188 3199	chum salmon
T19	LIVB 3201 3218	Oncorhynchus keta
T20	LIVB 3280 3289	Sika deer
T21	LIVB 3350 3361	pink salmon
T22	LIVB 3363 3375	O. gorbuscha
T23	LIVB 3381 3392	chum salmon
T24	LIVB 3518 3528	Brown bear
T25	LIVB 4001 4012	brown bears
T26	LIVB 4071 4082	brown bears

Use as python module

>>> from taxonerd import TaxoNERD
>>> ner = TaxoNERD(model="en_ner_eco_biobert", prefer_gpu=False, with_abbrev=False) # Add with_linking="gbif_backbone" or with_linking="taxref" to activate entity linking

Examples

Find taxonomic entities in an input string

>>> ner.find_entities("Brown bears (Ursus arctos), which are widely distributed throughout the northern hemisphere, are recognised as opportunistic omnivore")
       offsets          text
T0   LIVB 0 11   Brown bears
T1  LIVB 13 25  Ursus arctos

Find taxonomic entities in an input file

>>> ner.find_in_file("./test_txt/sample_text1.txt", output_dir=None)
T0	LIVB 4 21	pinewood nematode
T1	LIVB 29 55	Bursaphelenchus xylophilus
T2	LIVB 83 100	pine wilt disease
T3	LIVB 180 188	Serratia
T4	LIVB 326 348	Serratia grimesii BXF1
T5	LIVB 424 428	BXF1

Find taxonomic entities in all the files in the input directory, and write the results in the output directory

>>> ner.find_all_files("./test_txt", "./test_ann")

License

License: MIT

Authors

TaxoNERD was written by nleguillarme.