py37/nlp

Natural-language processing library

Natural Language Processing

Introduction

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.

Features

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.

Requirements

• kdb+>=? v3.5 64-bit
• Anaconda Python 3.x
• embedPy

Dependencies

The following python packages are required:

1. numpy
2. beautifulsoup4
3. spacy

To install these packages with

pip

pip install -r requirements.txt

or with conda

conda install --file requirements.txt

• Download the English model using python -m spacy download en

Installation

Run tests with

q test.q

Place the library file in $QHOME and load nlp/init.q

q)\l nlp/init.q
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

Docker

If you have Docker installed you can alternatively run:

$ docker run -it --name mynlp kxsys/nlp
kdb+ on demand - Personal Edition

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If applicable please provide your company name (press enter for none): ACME Limited
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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

Documentation

Documentation is available on the nlp homepage.

Status

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].