NLP (Natural Language Processing)

Data Platform

Natural Language Processing (NLP) is the term applied to Artificial Intelligence’s (AI) ability to understand spoken or written language. Natural Language Processing is a foundational software component enabling humans to interact with computers using voice or typed dialogs.

Why is Natural Language Processing Important?

Businesses have masses of text and audio information that can be made available to NLP for analysis. NLP can mine the unstructured data to give meaning and insights that can contribute to better-informed decision making. Social media feeds, for example, can be examined to understand the gist of a statement so a business can take proactive steps to protect it from reputational damage.

In the early days of computing, humans had to adapt to computer languages using instructions coded in binary or using compilers to translate instructions in assembler and higher-level languages. This limited the usefulness of computers to a small percentage of people. Over the decades, computers have become available to less technically skilled individuals thanks to personal computers and smartphones. Today, NLP leads web chat and phone-based customer interactions, reducing callers’ wait times and freeing advisors for more involved discussions.

In a retail analytics scenario, imagine a store manager asking questions on the fly like “Who were my top 10 customers this month?” or “What were our best-selling items this week?” without having to rely on custom reports.

The Five Steps of Natural Language Processing

Text processing by applications such as compilers needs to assess inputs using multiple passes, each with its discrete function. Below are these phases in order:

Lexical or Morphological Analysis

A lexicon is created of all the words which are grouped. Morphological analysis assigns the base meanings to words.

Syntax Analysis (Parsing)

Parsing the words checks the structure and grammar of statements. A syntax tree is constructed to ensure the statement makes sense.

Semantic Analysis

In this phase, the text is examined to understand the meaning of the text. To ensure that the data types are used in a way that’s consistent with their definition. Synonyms, antonyms, and homonyms are identified.

Discourse Integration

In this phase, the text is examined to determine the context and ensure that all the pieces of the text are consistent with that context. Each sentence is considered a contributor to the overall context contained in the text. Relationships between entities and topics are considered along with a thematic understanding and historical and social context.

Pragmatic Analysis

In this phase, the learnings from the previous phases are used to extract sufficient understanding to answer questions about the subject within the same context. This is critical for conversational tools such as chatbots.

Use Cases For Natural Language Processing

Below are some use cases for NLP:

  • Customer satisfaction analysis. Feedback forms often contain critical advice in the free text fields of a survey.
  • Product feedback can be obtained using surveys or simply analyzing social media feeds that can be assessed using natural language processing.
  • Interactive chatbots can have constructive dialogs with customers as the first line of contact when some problems or questions can be easily answered. In a sales situation, a customer may want to know more about a product that a chatbot can share. The chatbot can connect to a live sales representative to qualify the potential opportunity further.
  • Local language translation can be done more effectively using NLP to create the first pass draft, saving the most time-consuming phase of the task. The lower cost of translation and reduced time to publication means getting products to more markets faster.

Generative AI is the Next Step in NLP Maturity

We are seeing an explosion in the use of GPT-assisted interactions today. The phone-based chat programs are rapidly evolving into avatar-based interactions to make user interfaces even more relatable and accessible to more people with limited computer skills. Utilities are creating guided videos that explain bills clearly and can take questions interactively.

The importance of NLP (Natural Language Processing) is becoming as invaluable a tool as search engines were a decade ago because it is the foundation of machine understanding.

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