NLP Demo

This is a Demo project working on Natural Processing Language testing some fundamentals use cases of NLP such as:

• Sentiment Analysis: Try to predict if a text (i.e. review, comment) is positive or negative
• Question Answer: Given a large text, try to extract answers by asking questions on top of the text.
• Summarization: Summarize a text without losing the context.
• Word Analogy: Find the nearest neighbor word based in a corpus input
• Text Mining: This consists of extracting information from a text, such as verbs, person's name, entities, countries, places, etc.
• And some more

Most important dependecies:

1. Spacy IO - Industrial-Strength Natural Language Processing
2. Hugging Face - State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0
3. Pytorch - An open source machine learning framework that accelerates the path from research prototyping to production deployment.

The project is divided into two parts

1. NLP API - A Flask based api.
2. NLP Web APP - Vue Js application

Python API

• yelp_dataset(start=0, end=10) - return a range of reviews with starting and end postion. The reviews are located in dataset/yelp.csv file
• analyze(request) - Perform text mining on a POST request containing a text as input. The text mining are based on a configuration input array containing the desired mining actions: verbs, person, country etc
• sentiment_analysis(request) - Perform a sentiment analysis on a given text input and return a dictionary {LABEL, score}. LABEL can be either POSITIVE or NEGATIVE. The score is the acurracy probability
• answer_question(request) - inputs {text, question}. Extract answer from the input text based on the input question. Returns a answer dictionary {answer}
• summarizer(request) - inputs {text}. Summarize the input text. max_length for the summarization is round(int(len(data["text"]) * 0.2) / 2) and min_length is 30.

Examples

1. A question answering solution

2. Text Summarization

Note

The repository is still under work. This is just an intro README to give the big picture of the project.