Natural Language Processing - AI Blockchain Services

What is Natural Language Processing ?

What Can Businesses Use NLP Algorithms For ?

Natural Language Processing algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statistical inference. In general, the more data analyzed, the more accurate the model will be.

  • Summarize blocks of text to extract the most important and central ideas while ignoring irrelevant information. 
  • Create a chat bot, a language parsing deep learning model made by Google that uses Point-of-Speech tagging.
  • Automatically generate keyword tags from content which leverages LDA, a technique that discovers topics contained within a body of text.
  • Identify the type of entity extracted, such as it being a person, place, or organization using Named Entity Recognition.
  • Use Sentiment Analysis to identify the sentiment of a string of text, from very negative to neutral to very positive.
  • Reduce words to their root, or stem, or break up text into tokens

Natural Language Processing, Understanding uses intent, entities and utterances for human and machine correspondence.

Our Natural Language Processing Solutions

Automatic Translation

Automatic translation allows a computer to quickly translate a complex piece of text from one language into another. Because different languages are highly nuanced and idiosyncratic, this is an area where machine learning techniques are extremely useful. This is the technology that allows Google to automatically translate pages from French or Urdu or Mandarin into English.

Natural Language Generation

Natural Language Generation (NLG) combines data analysis and text generation to take data and turn it into language that humans can understand. While it’s been used to create jokes and poems, it’s also being used to generate news articles based on stock market events and weather reports based on meteorological data.

Topic Segmentation

Topic segmentation and information retrieval refer to the process of dividing text into meaningful units and identifying meaningful pieces of information based on a search query. Taken together, these two techniques are also being used by tech companies to create searchable databases of legal opinions, allowing lawyers to more efficiently find relevant case law.

Automatic Summarization

Automatic summarization is the process of creating a short summary of a longer piece of text that captures relevant information. Think of the executive summaries found at the beginnings of research papers and longer reports. This can be achieved by extracting key sentences and combining them into a concise paragraph, or by generating an original summary from keywords and phrases.

Speech Processing

Speech processing is the specific technology that allows virtual assistants to translate verbal commands into discrete actions for the computer to perform. This technology allows Amazon Echo to translate your request to hear some dance music into a specific Pandora search.

Sentiment Analysis

Sentiment analysis is routinely used by social analytics companies to put numbers behind the feelings expressed on social media or the web in order to generate actionable insights. Marketers use sentiment analysis to inform brand strategies, while customer service and product departments can use it to identify bugs, product enhancements, and possible new features.

Benefits to Choosing Natural Language Processing

More than just another trend or gimmick, Natural Language Processing has become a powerhouse in the realm of on-site search and has paved the way for a vast number of benefits reserved only for those who are intent on improving their business by advancing their site search capabilities.

1. Better results all the way around
Far and above any keyword matching or text-driven search, semantic search provides results that are true to form: exactly what your customers are looking for.

2. Search processing deciphers what your customers really mean
Your customers are human, which means they’re fallible. They make spelling errors, confuse brands with products and forget details — it’s up to your on-site search to bridge the gap.

3. More data mined means more data for growth
Measuring what your customers are searching is key in improving your business. Through the tremendous depth of data presented by NLP, you’re able to cultivate that data to a huge degree, learning about customer habits and tendencies across your entire consumer base.

4. Complex search capabilities eliminate ineffective results
Being able to process numerous variables in a single search means providing a cumulative result that’s indicative of your customer’s end requirements. Natural language processing looks at the whole picture, not just the individual keywords in a search, providing results that are the sum of their parts.

5. Contextual understanding delivers answers
Today’s search engines are slowly becoming Q&A boxes — customers ask questions and expect answers. Thanks to the complex search capabilities afforded by NLP, your customers can ask questions freely and get the products they’re looking for.

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