Citizens Document Clustering

• Document clustering for Citizens Foundation.

• Built using document embeddings from Gensim by RaRe-Technologies.

• Our Doc2Vec model assumes lemmas as input, although inflected words work, too.

• Icelandic texts are lemmatized using ice_lemmatizer.py, which is built on Reynir by Mideind.

• English texts are lemmatized using en_lemmatizer.py, which is supported by spaCy.

• This repository is a work in progress.

Doc2Vec

Includes:

• A script to train a Doc2Vec model.
• A script to test a Doc2Vec model.
• A script to infer a vector from a previously unseen document.
• A script to get the similarity (float) between all docs in the model.
• A couple of short texts (not suited for training a reliable model) used for testing.
• NOTE: This plot is only an example to show the relations between the files.

Spelling

Includes:

• A script to see if a word is split in two, a common spelling mistake in Icelandic.
  • bílakjallari | *bíla kjallari
• A script that catches spelling mistakes, based on Word2Vec and probability.

Word2Vec

Includes:

• A script to train a Word2Vec model.
  • As of now, the model is only used for correction of spelling mistakes.
  • Might be used to classify documents further based on keyword vectors.

License

AGPL