NLP Tutorial 16 – CV and Resume Parsing with Custom NER Training with SpaCy
In this video we will see CV and resume parsing with custom NER training with SpaCy. Natural Language Processing (NLP) is the field of Artificial Intelligence, where we analyse text using machine learning models. Text Classification, Spam Filters, Voice text messaging, Sentiment analysis, Spell or grammar check, Chat bot, Search Suggestion, Search Autocorrect, Automatic Review, Analysis system, Machine translation are the applications of NLP.
If a normal data analysis tool in Python for tabular and structured data has Pandas, then the data analysis tool in Natural Language Processing (NLP) for text and unstructured data has spaCy.When you’re first starting out as a data scientist, chances are you’ll be dealing with structured data which doesn’t necessarily require spaCy to handle complicated text data, depending on your needs.In most cases — Pandas will fit your usage as it’s powerful enough to do most of the data cleaning and analysis for structured data. Once you start dealing with unstructured text data — basically NLP stuff — where this can no longer be handled by Pandas, this is when spaCy comes in with tons of in-built capabilities to process, analyze and even understand these data through sophisticated and efficient NLP techniques.
🔊 Watch till last for a detailed description
02:14 what is Resume summarization?
13:56 Loading the data
25:26 Load the model
34:32 Analysing output
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