Stanford CoreNLP/Stanza:

Python Flask server that checks for input from a text file containing the results from MozillaDeepSpeech and performs the following Natural Language Processing operatiopns using the StanfordNLP library, currently known as Stanza:

• TokenizeProcessor.
• POSProcessor.
• LemmaProcessor.
• NERProcessor.

Pre-Reqs:

• Need to have Python3 installed and pip3.

• Need to set up a virtual environment in Python skip to 'Activate' if you already have one set up.

  • Use the following command to install virtual-env: (Linux) sudo apt install python3-venv (Windows) pip3 install virtualenv
  • Create a virtual directory which has the required scripts using: python3 -m venv my-venv
  • Activate the virtual env using the following command: source my-venv/bin/activate

• Need to have flask package installed using: (Linux) sudo apt-get install flask (Windows)pip3 install flask (Mac) pip3 install flask

• Need to have flask_apscheduler package installed using: (Windows)pip3 install flask_apscheduler (Mac) pip3 install flask_apscheduler

• Need to have datetime package installed using: (Windows)pip3 install datetime (Mac) pip3 install datetime

• Need to have stanza package installed using: (Linux) sudo apt-get install stanza (Windows)pip3 install stanza (Mac) pip3 install stanza

• Need to download the pre-trained English model and extract it using: Launch the Python interactive interpreter and first, run import stanza then, stanza.download('en', '~/stanza_resources') *By default, Stanza stores its models in a folder in your home directory. The English model should be downloaded to off-top-python/stanfordnlp. *Make sure to specify your own directory (the second argument in the download command. *Make sure the name of the model folder is "stanza_resources".

Description:

• Inside this branch navigate into stanfordnlp. You will see a script called stanza-nlp.py which is a Flask server. stanza-nlp.py - The Flask server runs through a task scheduler, checks for input from a text file, and displays the processed results (Tokens, POS', NER tags) on the terminal every 10 seconds.

How to use: Locate the directory of stanza-nlp.py inside stanfordnlp and then run python3 stanza-nlp.py. This will automatically run the processes and generate the results on the terminal. *Make sure you have the input .txt file in the same directory which is the same file that Mozilla DeepSpeech generated with the results.