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spaCy ANN Linker, a pipeline component for generating spaCy KnowledgeBase Alias Candidates for Entity Linking.

Project description

spaCy ANN Linker, a pipeline component for generating spaCy KnowledgeBase Alias Candidates for Entity Linking based on an Approximate Nearest Neighbors (ANN) index computed on the Character N-Gram TF-IDF representation of all aliases in your KnowledgeBase.

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spaCy ANN Linker is a spaCy a pipeline component for generating alias candidates for spaCy entities in doc.ents. It provides an optional interface for linking ambiguous aliases based on descriptions for each entity.

The key features are:

  • Easy spaCy Integration: spaCy ANN Linker provides completely serializable spaCy pipeline components that integrate directly into your existing spaCy model.

  • CLI for simple Index Creation: Simply run spacy_ann create_index with your data to create an Approximate Nearest Neighbors index from your data, make an ann_linker pipeline component and save a spaCy model.

  • Built in Web API for easy deployment and Batch Entity Linking queries


Python 3.6+

spaCy ANN Linker is convenient wrapper built on a few comprehensive, high-performing packages.


$ pip install spacy-ann-linker
---> 100%
Successfully installed spacy-ann-linker

Data Prerequisites

To use this spaCy ANN Linker you need pre-existing Knowledge Base data. spaCy ANN Linker expects data to exist in 2 JSONL files together in a directory

│   aliases.jsonl
│   entities.jsonl

For testing the package, you can use the example data in examples/tutorial/data

│   aliases.jsonl
│   entities.jsonl

entities.jsonl Record Format

{"id": "Canonical Entity Id", "description": "Entity Description used for Disambiguation"}

Example data

{"id": "a1", "description": "Machine learning (ML) is the scientific study of algorithms and statistical models..."}
{"id": "a2", "description": "ML (\"Meta Language\") is a general-purpose functional programming language. It has roots in Lisp, and has been characterized as \"Lisp with types\"."}
{"id": "a3", "description": "Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data."}
{"id": "a4", "description": "Neuro-linguistic programming (NLP) is a pseudoscientific approach to communication, personal development, and psychotherapy created by Richard Bandler and John Grinder in California, United States in the 1970s."}

aliases.jsonl Record Format

{"alias": "alias string", "entities": ["list", "of", "entity", "ids"], "probabilities": [0.5, 0.5]}

Example data

{"alias": "ML", "entities": ["a1", "a2"], "probabilities": [0.5, 0.5]}
{"alias": "Machine learning", "entities": ["a1"], "probabilities": [1.0]}
{"alias": "Meta Language", "entities": ["a2"], "probabilities": [1.0]}
{"alias": "NLP", "entities": ["a3", "a4"], "probabilities": [0.5, 0.5]}
{"alias": "Natural language processing", "entities": ["a3"], "probabilities": [1.0]}
{"alias": "Neuro-linguistic programming", "entities": ["a4"], "probabilities": [1.0]}

Example Data

spacy-ann-linker comes with some example data to get you started.

!!! important If this is your first time using spacy-ann-linker start out with the example data using the spacy_ann example_data command. Just pass an output_dir to write the example data to.

$ spacy_ann example_data ./kb

=============== Example Data ================
Writing Example data to test/kb
✔ Done.

This should leave you with a folder called ./kb_dir that has a structure like

│   aliases.jsonl
│   entities.jsonl

spaCy prerequisites

If you don't have a pretrained spaCy model, download one now. The model needs to have vectors so download a model bigger than en_core_web_sm

$ spacy download en_core_web_md
---> 100%
Successfully installed en_core_web_md

Next Steps

Once you have the Data and spaCy prerequisites completed follow along with the Tutorial to for a step-by-step guide for using the spacy_ann package.

!!! important These are just the prerequisites. Follow the full tutorial linked above for a step-by-step guide to working with spacy-ann-linker.


This project is licensed under the terms of the MIT license.

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