Natural Language Processing

Large Volume of Textual Data

Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.

Structuring A Highly Unstructured Data Source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang.

The Role of NLP in Enhancing Language Understanding and Application

While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

Everyday NLP Samples

Beyond conversing with virtual assistants like Alexa or Siri, here are a few more examples:

• Spam Filters used in emails and messaging applications. You’re seeing Bayesian spam filtering, a statistical NLP technique that compares the words in spam to valid emails to identify junk mail.
• Using its built-in search bar to search and navigate through a website. You’ve used NLP methods for search, topic modeling, entity extraction and content categorization.
• Have you used transcript apps to write the words you’re dictating to skip writing? You’ve used speech-to-text conversion, an NLP capability.
• Issue voice command to your television and other iot at home
• Engaged with a chatbot in many shopping apps and social channels

NLP and Text Analysis

Beyond conversing with virtual assistants like Alexa or Siri, here are a few more examples:

• Social media analytics. Track awareness and sentiment about specific topics and identify key influencers.
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  Sentiment Analysis. Analyse customer feedback and attitiude towards your brand and product
  
• Text analytics, document analysis, and sentiment analysis
• Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa.
• Machine translation. Automatic translation of text or speech from one language to another.
• Document summarization. Automatically generating synopses of large bodies of text.