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Adding input text proccessing / resolving issue #23
Latest commit 0f7e109 May 11, 2018

pke - python keyphrase extraction

pke is an open source python-based keyphrase extraction toolkit. It provides an end-to-end keyphrase extraction pipeline in which each component can be easily modified or extented to develop new approaches. pke also allows for easy benchmarking of state-of-the-art keyphrase extraction approaches, and ships with supervised models trained on the SemEval-2010 dataset.

pke is compatible with Python 2.7 (Python 3.6 is WIP)

If you use pke, please cite the following paper:

Table of Contents


The following modules are required:


To pip install pke from github:

pip install git+

Minimal example

pke provides a standardized API for keyphrases from a document. Start by typing the 5 lines below. For using another model, simply replace pke.unsupervised.TopicRank with another model (list of implemented models).

import pke

# initialize keyphrase extraction model, here TopicRank
extractor = pke.unsupervised.TopicRank(input_file='/path/to/input')

# load the content of the document, here document is expected to be in raw
# format (i.e. a simple text file) and preprocessing is carried out using nltk

# keyphrase candidate selection, in the case of TopicRank: sequences of nouns
# and adjectives

# candidate weighting, in the case of TopicRank: using a random walk algorithm

# N-best selection, keyphrases contains the 10 highest scored candidates as
# (keyphrase, score) tuples
keyphrases = extractor.get_n_best(n=10, stemming=False)

A detailed example is provided in the examples/ directory.


Input formats

pke currently supports the following input file formats (examples of formatted input files are provided in the examples/ directory):

  1. raw text: text preprocessing (i.e. tokenization, sentence splitting and POS-tagging) is carried out using nltk.

    Example of raw text:

    Efficient discovery of grid services is essential for the success of
    grid computing. [...]

    To read raw text document:

  2. preprocessed text: whitespace-separated POS-tagged tokens, one sentence per line.

    Example of preprocessed text:

    Efficient/NNP discovery/NN of/IN grid/NN services/NNS is/VBZ essential/JJ for/IN the/DT success/NN of/IN grid/JJ computing/NN ./.

    To read preprocessed text document:

  3. Stanford XML CoreNLP: output file produced using the annotators tokenize, ssplit, pos and lemma. Document logical structure information can by specified by incorporating attributes into the sentence elements of the CoreNLP XML format.

    Example of CoreNLP XML:

    <?xml version="1.0" encoding="UTF-8"?>
          <sentence id="1" section="abstract" type="bodyText" confidence="0.925">
              <token id="1">
              <token id="2">

    Here, the document logical structure information is added to the CoreNLP XML output by the use of the section, type and confidence attributes. We use the classification categories proposed by Luong et al. (2012). In pke, document logical structure information is exploited by the WINGNUS model and the following values are handled:

    section="title|abstract|introduction|related work|conclusions"

    To read a CoreNLP XML document:


Implemented models

pke currently implements the following keyphrase extraction models:

Already trained supervised models

pke ships with a collection of already trained models (for supervised keyphrase extraction approaches) and document frequency counts that were computed on the training set of the SemEval-2010 benchmark dataset. These are located into the pke/models/ directory.

For details about the provided models, see pke/models/

These already trained models/DF counts are used by default if no parameters are given.

Document Frequency counts

pke ships with document frequency counts computed on the SemEval-2010 benchmark dataset. These counts are used in various models (TfIdf, KP-Miner, Kea and WINGNUS). The following code illustrates how to compute new counts from another (or a larger) document collection:

from pke import compute_document_frequency
from string import punctuation

# path to the collection of documents
input_dir = '/path/to/input/documents'

# path to the DF counts dictionary, saved as a gzip tab separated values
output_file = '/path/to/output/'

# compute df counts and store stem -> weight values
                           format="corenlp",            # input files format
                           use_lemmas=False,    # do not use Stanford lemmas
                           stemmer="porter",            # use porter stemmer
                           stoplist=list(punctuation),            # stoplist
                           delimiter='\t',            # tab separated output
                           extension='xml',          # input files extension
                           n=5)              # compute n-grams up to 5-grams

DF counts are stored as a ngram tab count file. The number of documents in the collection, used to compute Inverse Document Frequency (IDF) weigths, is stored as an extra line --NB_DOC-- tab number_of_documents. Below is an example of such a file:

--NB_DOC--  100
greedi alloc  1
sinc trial structur 1
complex question  1

Newly computed DF counts should be loaded and given as parameter to candidate_weighting() for unsupervised models and feature_extraction() for supervised models:

import pke

# initialize TfIdf model
extractor = pke.unsupervised.TfIdf(input_file='/path/to/input')

# load the DF counts from file
df_counts = pke.load_document_frequency_file(input_file='/path/to/file')

# load the content of the document

# keyphrase candidate selection

# candidate weighting with the provided DF counts

# N-best selection, keyphrases contains the 10 highest scored candidates as
# (keyphrase, score) tuples
keyphrases = extractor.get_n_best(n=10)

A detailed example for computing new DF counts is given in examples/

Training supervised models

Here is a minimal example for training a new supervised model:

import pke

# load the DF counts from file
df_counts = pke.load_document_frequency_file('/path/to/file')

# train a new Kea model
                           model=pke.supervised.Kea()) # here we train a Kea model

The training data consists of a set of documents along with a reference file containing annotated keyphrases in the SemEval-2010 format.

A detailed example for training a supervised model is given in examples/

Extracting keyphrases from an input text

While pke is first intended to process input files, it can be used to directly extract keyphrases from a given input text:

import pke

extractor = pke.unsupervised.TopicRank()

input_text = u"""Keyphrase extraction is the task of identifying single or
                 multi-word expressions that represent the main topics of a
                 document. In this paper we present TopicRank, a graph-based
                 keyphrase extraction method that relies on a topical
                 representation of the document."""

keyphrases = extractor.get_n_best(n=10, stemming=False)

Non English languages

While the default language in pke is English, extracting keyphrases from documents in other languages is easily achieved by inputting already preprocessed documents, and setting the language parameter to the desired language. The only language dependent resources used in pke are the stoplist and the stemming algorithm from nltk that is available in 11 languages.

Given an already preprocessed document (here in French):

France/NPP :/PONCT disparition/NC de/P Thierry/NPP Roland/NPP [...]
Le/DET journaliste/NC et/CC commentateur/NC sportif/ADJ Thierry/NPP [...]
Commentateur/NC mythique/ADJ des/P+D matchs/NC internationaux/ADJ [...]

Keyphrase extraction can then be performed by:

import pke

# initialize TopicRank and set the language to French (used during candidate
# selection for filtering stopwords)
extractor = pke.unsupervised.TopicRank(input_file='/path/to/input',

# load the content of the document and perform French stemming (instead of
# Porter stemmer)
extractor.read_document(format='preprocessed', stemmer='french')

# keyphrase candidate selection, here sequences of nouns and adjectives
# defined by the French POS tags NPP, NC and ADJ
extractor.candidate_selection(pos=["NPP", "NC", "ADJ"])

# candidate weighting, here using a random walk algorithm

# N-best selection, keyphrases contains the 10 highest scored candidates as
# (keyphrase, score) tuples
keyphrases = extractor.get_n_best(n=10)


We evaluate the performance of our re-implementations using the SemEval-2010 benchmark dataset. This dataset is composed of 244 scientific articles (144 in training and 100 for test) collected from the ACM Digital Library (conference and workshop papers). Document logical structure information, required to compute features in the WINGNUS approach, is annotated with ParsCit. The Stanford CoreNLP pipeline (tokenization, sentence splitting and POS-tagging) is then applied to the documents from which irrelevant pieces of text (e.g. tables, equations, footnotes) were filtered out. The dataset we use (lvl-2) can be found at

We follow the evaluation procedure used in the SemEval-2010 competition and evaluate the performance of each implemented approach in terms of precision (P), recall (R) and f-measure (F) at the top 10 keyphrases. We use the set of combined (stemmed) author- and reader-assigned keyphrases as reference keyphrases.

Approach P@10 R@10 F@10
TfIdf 20.0 14.1 16.4
TopicRank 15.6 10.8 12.6
SingleRank 2.2 1.5 1.8
KP-Miner 24.1 17.0 19.8
Kea 23.5 16.6 19.3
WINGNUS 24.7 17.3 20.2

Code documentation

For code documentation, please visit