spacy_ke: Keyword Extraction with spaCy.

⏳ Installation

pip install spacy_ke

🚀 Quickstart

Usage as a spaCy pipeline component (spaCy v2.x.x)

import spacy
import spacy_ke

# load spacy model
nlp = spacy.load("en_core_web_sm")

# spacy v3.0.x factory.
# if you're using spacy v2.x.x swich to `nlp.add_pipe(spacy_ke.Yake(nlp))`
nlp.add_pipe("yake")

doc = nlp(
    "Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence "
    "concerned with the interactions between computers and human language, in particular how to program computers "
    "to process and analyze large amounts of natural language data. "
)

for keyword, score in doc._.extract_keywords(n=3):
    print(keyword, "-", score)

Configure the pipeline component

Normally you'd want to configure the keyword extraction pipeline according to its implementation.

window: int = 2  # default
lemmatize: bool = False  # default
candidate_selection: str = "ngram"  # default, use "chunk" for noun phrase selection.

nlp.add_pipe(
    Yake(
        nlp,
        window=window,  # default
        lemmatize=lemmatize,  # default
        candidate_selection="ngram"  # default, use "chunk" for noun phrase selection
    )
)

And if you want to define a custom candidate selection use the example below.

from typing import Iterable
from spacy.tokens import Doc
from spacy_ke.util import registry, Candidate


@registry.candidate_selection.register("custom")
def custom_selection(doc: Doc, n=3) -> Iterable[Candidate]:
    ...


nlp.add_pipe(
    Yake(
        nlp,
        candidate_selection="custom"
    )
)

Development

Set up virtualenv

$ python -m venv .venv
$ source .venv/bin/activate

Install dependencies

$ pip install -U pip
$ pip install -r requirements-dev.txt

Run unit test

$ pytest

Run black (code formatter)

$ black spacy_ke/ --config=pyproject.toml

Release package (via twine)

$ python setup.py upload

References

[1] A Review of Keyphrase Extraction

@article{DBLP:journals/corr/abs-1905-05044,
  author    = {Eirini Papagiannopoulou and
               Grigorios Tsoumakas},
  title     = {A Review of Keyphrase Extraction},
  journal   = {CoRR},
  volume    = {abs/1905.05044},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.05044},
  archivePrefix = {arXiv},
  eprint    = {1905.05044},
  timestamp = {Tue, 28 May 2019 12:48:08 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-05044.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

[2] pke: an open source python-based keyphrase extraction toolkit.

@InProceedings{boudin:2016:COLINGDEMO,
  author    = {Boudin, Florian},
  title     = {pke: an open source python-based keyphrase extraction toolkit},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  pages     = {69--73},
  url       = {http://aclweb.org/anthology/C16-2015}
}