WebCrawler and NLP System - Apply NLP Tasks in Python - IT Assignment Help

Assignment Task:

Task:

Overview
This assignment involves building parts of a prototype NLP solution and investigating the deployment of the NLP solution that could be used by a development team. The initial part of the NLP solution is gathering data using a web scraper. The web scraper collects information from relevant websites and supplements that website data with metadata from additional knowledge databases. Once the data for the NLP solution is gathered, the data need to be processed and cleaned for NLP tasks.

The NLP tasks use the harvested data to solve or investigate a wider NLP issue. Here, you are to apply two NLP tasks, such as Name Entity Recognition, Semantic analysis, Sentiment analysis, etc. The output of one of the NLP tasks may even be one of the inputs to the other NLP task. Once the two NLP tasks and web crawler have been written and documented, these project components need to be made accessible to a wider development team.

To assist a development team integrating your WebCrawler and NLP tasks, you will need to publish your documentation and code in a Git-repository. Additionally, you will provide a high-level, end-to-end practical data science solution design video presentation.

Learning outcomes
Apply NLP data science skills, knowledge, and techniques to solve problems in data science NLP projects with a focus on web crawler and content extraction from webpages.
 

Apply NLP tasks in Python
Understand how to deploy data science projects into production pipelines Effectively communicate the results of the project as a video Work-based skills
The ability to extract key values (e.g. structured data) from HTML Image, video, and text recognition (e.g. unstructured data). Undertake applied industry research
 

Background
Many NLP solutions are comprised of multiple NLP tasks jointly working together. The auto complete function in a programming IDE is an example where there is an NLP NER task identifying the names of the variables and functions which feeds into an NLP semantic parser to trap simple errors before compiling/running. Another example is website forms. On many web forms, address matching and prediction NLP tasks jointly run on incomplete user input to suggest addresses. Predictive addressing is a useful tool to ensure accurate user input and mitigates human errors.

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