Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software.The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.The development of NLP applications is challenging because computers traditionally require humans to “speak” to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. Human speech, however, is not always precise — it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.

How natural language processing works: techniques and tools              

Syntax and Semantic analysis are two main techniques used with natural language processing. Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules. Syntax techniques used include parsing (grammatical analysis for a sentence), word segmentation (which divides a large piece of text to units), sentence breaking (which places sentence boundaries in large texts), morphological segmentation (which divides words into groups) and stemming (which divides words with inflection in them to root forms).

Semantics involves the use and meaning behind words. NLP applies algorithms to understand the meaning and structure of sentences. Techniques that NLP uses with semantics include word sense disambiguation (which derives meaning of a word based on context), named entity recognition (which determines words that can be categorized into groups), and natural language generation (which will use a database to determine semantics behind words).

Current approaches to NLP are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. Deep learning models require massive amounts of labeled data to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to NLP currently.

Earlier approaches to NLP involved a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers’ intent from many examples, almost like how a child would learn human language.

Our Use Cases of Natural Language Processing

Use Cases of Natural Language Processing

Natural Language Processing (NLP) helps millions of people do some of the most common tasks, even without even knowing they’re using it. Spell check, predictive text, text-to-voice and vice versa, auto-replies and spam filters all use NLP to code, sift and sort through digital written material and automate these functions.

Tagging of Data to Empower Recommendation

We have used NLP to interpret free text and make it analyzable. There is a tremendous amount of information stored in raw text data. We have applied NER (Named Entity Recognition) to extract people, event, location and organization tags from raw text news articles. It was very hard for news editorial team to associate relevant tags with an news article but via applying NER we associated all relevant tags with news articles. This tagging of data empowered our recommendation engine to recommend most relevant articles.

Sentimental Analysis

Using sentiment analysis, our data scientists assessed comments on social media to see how their business’s brand is performing, for example, review notes from customer service teams to identify areas where people want the business to perform better.