Skip to main content

Automatic Keyword Extraction from Document Collections

Project description

Distiller
=========

Distiller provides convenient auto-extraction of document key words
based on term-frequency/inverse-document-frequency (TF-IDF) and word
positioning.

Distiller handles all of the pre-processing details and produces final
statistic reports in JSON format.


Requirements
------------

Distiller uses the [Natural Language Toolkit](http://www.nltk.org/)

You will need to download a couple of NLTK packages:

>>> import nltk
>>> nltk.download()
Downloader> d
Download which package (l=list; x=cancel)?
Identifier> maxent_treebank_pos_tagger
Downloader> d
Download which package (l=list; x=cancel)?
Identifier> stopwords



Installation
------------

Installation using pip:

$ pip install Distiller


Usage
-----

Typical usage from within the Python interpreter:

>>> from Distiller.distiller import Distiller
>>> distiller = Distiller(data, target, options)


Arguments
---------

### data

Path to file containing the document collection in JSON format.

{
'metadata': {
'base_url': 'The document's source URL (if any)'
},
'documents': [
{
'id': 'The document's unique identifier (if any)',
'body': 'The entire body of the document in a single text blob.',
}, ...
]
}

###target

Path where Distiller will output the following reports:

keywords: A list of words and the frequency with which they were detected as being keywords of documents.

bigrams: A list of word pairs and the frequency with which they were detected as being key pairs in documents.

trigrams: A list of word triples and the frequency with which they were detected as being key pairs in documents.

docmap: A mapping of document IDs to their respective keywords, n-grams, and other statistics.

keymap: A mapping of keywords to the documents they appear in.


###options

An optional dictionary containing document processing arguments in this format:

{
'normalize': True, # normalize tokens during pre processing
'stem': True, # stems tokens during pre processing
'lemmatize': False, # lemmatize during pre processing
'tfidf_cutoff': 0.001, # cutoff value to use for term-freq/doc-freq score
'pos_list': ['NN','NNP'], # POS white list used to filter for candidates
'black_list': [] # token list used to filter out from candidates
}

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

Distiller-0.1.1.1.tar.gz (9.6 kB view hashes)

Uploaded Source

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