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https://github.com/alan-turing-institute/netts

Toolbox for creating networks capturing semantic content of speech transcripts.
https://github.com/alan-turing-institute/netts

graph-theory hut23 networks nlp python semantic-content transcripts

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Toolbox for creating networks capturing semantic content of speech transcripts.

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README 

        
# netts - NETworks of Transcript Semantics



[![GitHub release](https://img.shields.io/github/v/release/alan-turing-institute/netts?include_prereleases)](https://GitHub.com/alan-turing-institute/netts/releases/)

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/netts.svg)](https://pypi.python.org/pypi/netts/)

[![codecov](https://codecov.io/gh/alan-turing-institute/netts/branch/main/graph/badge.svg?token=58uMq5hbNt)](https://codecov.io/gh/alan-turing-institute/netts)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)



Toolbox for constructing semantic speech networks from speech transcripts.



## About



The algorithms in this toolbox create a semantic speech graph from transcribed speech. Speech transcripts are short paragraphs of largely raw, uncleaned speech-like text. For example:



> 'I see a man and he is wearing a jacket. He is standing in the dark against a light post. On the picture there seems to be like a park and... Or trees but in those trees there are little balls of light reflections as well. I cannot see the... Anything else because it’s very dark. But the man on the picture seems to wear a hat and he seems to have a hoodie on as well. The picture is very mysterious, which I like about it, but for me I would like to understand more about the picture.'

> -- Example Transcript



Below is the semantic speech graph constructed from this text.



![Semantic speech graph example](https://github.com/alan-turing-institute/netts/raw/main/docs/docs/img/real_example_network_with_picture_transcript.png)

*Figure 1. Semantic Speech Graph. Nodes represents an entity mentioned by the speaker (e.g. I, man, jacket). Edges represent relations between nodes mentioned by the speaker (e.g. see, has on).*



## Getting started



Read the full documentation [here](https://alan-turing-institute.github.io/netts/).



### Where to get it



You can install the latest release from [PyPi](https://pypi.org/project/netts/)



```bash

pip install netts

```



or get the latest development version from GitHub (not stable)



```bash

pip install git+https://github.com/alan-turing-institute/netts

```



### Additional dependencies



Netts requires the Java Runtime Environment. Instructions for downloading and installing for your operating system can be found [here](https://docs.oracle.com/goldengate/1212/gg-winux/GDRAD/java.htm#BGBFHBEA).



Netts also requires a few additional dependencies to work which you can download with the netts CLI that was installed by pip



```bash

netts install

```



### Basic usage



The quickest way to process a transcript is with the CLI.



```bash

netts run transcript.txt outputs

```



where `transcript.txt` is a text file containing transcribed speech and `outputs` is the name of a directory to write the outputs to. Additional logging information can be found in `netts_log.log`.



## Contributors



Netts was written by [Caroline Nettekoven](https://www.caroline-nettekoven.com) in collaboration with [Sarah Morgan](https://semorgan.org).



Netts was packaged in collaboration with [Oscar Giles](https://www.turing.ac.uk/people/researchers/oscar-giles), [Iain Stenson](https://www.turing.ac.uk/research/research-engineering/meet-the-team) and [Helen Duncan](https://www.turing.ac.uk/people/research-engineering/helen-duncan).