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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis.
Some of the major tasks that are a part of NLP include:
· Automatic summarization
· Coreference resolution
· Discourse analysis
· Machine translation
· Morphological segmentation
· Named entity recognition (NER)
· Natural language generation
· Natural language understanding
· Optical character recognition (OCR)
· Part-of-speech tagging
· Parsing
· Question answering
· Relationship extraction
· Sentence breaking (also known as sentence boundary disambiguation)
· Sentiment analysis
· Speech recognition
· Speech segmentation
· Topic segmentation and recognition
· Word segmentation
· Word sense disambiguation
· Lemmatization
· Native-language identification
· Stemming
· Text simplification
· Text-to-speech
· Text-proofing
· Natural language search
· Query expansion
· Automated essay scoring
· Truecasing
Let’s discuss some of the cool things NLP helps us with in life
1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP!
2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand.
3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment
4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate.
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Muhammed Furkan Ataç says:

Someone needs to buy a microphone

Abhilash Kr says:

Link to slides please

Przemysław Posiadała says:

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