Skip to main content

Keyword extraction from linguistic publications

Project description

Keyword extraction from langsci publications

langscikw is a Python package and command line tool for bigram keyterm extraction. It is optimized for long, English, linguistic publications and can also be applied to TeX code.

Keyword extraction is done in three steps. No preprocessing is needed.

  • Step 1: KWE from the input document using YAKE. This is the simplest step as it doesn't need a corpus and should extract the most important keywords.
  • Step 2: KWE using TF-IDF trained on a raw TeX corpus. This step yields some more general keywords relevant to the linguistic discipline.
  • Step 3: KWE using TF-IDF trained on a detexed corpus. This step fills in some missing keywords that also appear in a reference corpus of 10,000 previously accepted keywords, keywordslist.txt.

The number of steps can be controlled by (not) providing the relevant corpora during training. The result needs some manual correction and supplementation of relevant unigrams or trigrams.


pip3 install langscikw

Developed in Python 3.7.3 32-bit. Needs at least Python 3.7 and the following packages: jellyfish, joblib, networkx, scikit-learn, segtok, regex.

Download the langsci corpus files from here or use your own.


Command line

The command line tool provides only a simple interface. If you'd like to customize the model parameters or the number of steps, please see below.

langscikw inputfile [n] [corpus1] [corpus2] [keywordslist] [--silent]

The keywords are printed to the console and can be redirected to a text file.


  • inputfile: Path to a .txt or .tex file or directory from which to extract keywords.
  • n: Optional Number of keywords to extract. Defaults to 300.
  • corpus1: Optional Path to corpus for step 2, usually raw TeX files or a joblib-compressed file. If not provided, looks for corpus_tex.gz in the current directory.
  • corpus2: Optional Path to corpus for step 3, usually detexed files or a joblib-compressed file. If not provided, looks for corpus_detexed.gz in the current directory.
  • keywordslist: Optional Path to a list of gold keywords for step 3. A default list based on langsci publications is installed with the package.
  • --silent: Optional Only print the result to the console, no progress updates.

KWE class

import langscikw
input_path = "my_book"              # File/directory to extract keywords from
keywords_path = "keywords.txt"      # File to save keywords to

kwe = langscikw.KWE()
kwe.train("corpus_tex.gz", "corpus_detexed.gz")
kws = kwe.extract_keywords(input_path, n=300, dedup_lim=0.85)
for kw in kws:
    print(kw)                       # Keywords are alphabetically sorted strings

Keyword arguments

  • n: Optional Number of keywords to extract. Defaults to 300.
  • dedup_lim: Optional Deduplication limit. Keywords that have a Jaro-Winkler Similarity of >dedup_lim are not added to the final list. Defaults to 0.85.

Stand-alone models

The YAKE and TF-IDF models may also be used on their own. Please consult the docstrings for more information.

import langscikw
yake = langscikw.yakemodel.YakeExtractor()      # -> extract_keywords()
tfidf = langscikw.tfidfmodel.TfidfExtractor()   # -> train() -> extract_keywords()

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

langscikw-0.0.1.tar.gz (90.1 kB view hashes)

Uploaded source

Built Distribution

langscikw-0.0.1-py3-none-any.whl (90.3 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page