Natural language processing (NLP) can be used to answer a variety of questions about unstructured text, as well as facilitating open-ended exploration. It can be applied to datasets such as emails, online articles and comments, tweets and novels. Although the source is text, transformations are applied to convert this data to vectors, dictionaries and symbols which can be handled very effectively by q. Many operations such as searching, clustering, and keyword extraction can all be done using very simple data structures, such as feature vectors.
The NLP allows users to parse dataset using the spacy model from python in which it runs tokenisation, Sentence Detection, Part of speech tagging and Lemmatization. In addition to parsing, users can cluster text documents together using different clustering algorithms like MCL, K-means and radix. You can also run sentiment analysis which indicates whether a word has a positive or negative sentiment.
For full documentation, go to nlp
Clone the NLP repo to $QHOME and load using
q)\l nlp/init.q
If you have Docker installed you can alternatively run:
$ docker run -it --name mynlp kxsys/nlp
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Loading utils.q
Loading regex.q
Loading sent.q
Loading parser.q
Loading time.q
Loading date.q
Loading email.q
Loading cluster.q
Loading nlp.q
q).nlp.findTimes"I went to work at 9:00am and had a coffee at 10:20"
09:00:00.000 "9:00am" 18 24
10:20:00.000 "10:20" 45 50
N.B. instructions regarding headless/presets are available
N.B. build instructions for the image are available
The following python packages are required:
- numpy
- beautifulsoup4
- spacy
To install these packages,run $pip install -r requirements.txt
- Download the English model using
python -m spacy download en
The nlp library is still in development and is available here as a beta release.
If you have any issues, questions or suggestions, please write to [email protected].