NLP: Understanding the basics and applications

Artificial intelligence (AI) is omnipresent and is changing the way we look at the world. However, the advent of AI and data analytics tools has led to the boom of data. And to process this mountain of raw data, we need Natural Language Processing. In technical parlance, NLP is a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements, helps in creating and reading textual data, visual data, and more. Basically, it helps computers understand, interpret, and manipulate human language. From automatic translation or sentence completion to identify insurance fraud and powering chatbots, NLP is used almost everywhere. The main objective of NLP applications is to help humans have interaction with computers as they would with another human.

Types

NLP consists of two basic divisions, viz., Natural Language Understanding and Natural Language Generation. Natural Language Understanding is the analytical branch of the Natural Language Processing. It is all about analyzing the contents of the text and understanding its insights. Meanwhile, Natural Language Generation is the operational branch of NLP, built for enhancing computer's capability to generate text, whether by converting data to written language, translating speech to written text, or converting text to audible speech.

Challenges

Most of the NLP applications involve four necessary steps. These are sentence segmentation, word tokenization, part of speech or morph syntactic tagging, and syntactic or dependency parsing. However, certain challenges can hinder the functioning of NLP software. These are primarily due to irregularity and ambiguity of natural language. The language used by humans contains a multitude of words with numerous alternative uses, dialects, and more. While we are capable of expressing, perceiving, and interpreting very elaborate and nuanced meanings, machines need a clear set of well-defined algorithms to follow. The second challenge is the mutable nature of language structures, which therefore makes it complicated. Various phrase types can be formed out of the same bag of words. Hence, sometimes, one-time sentence boundary disambiguation can be difficult to achieve.

Applications

While the simple function of NLP is to facilitate understanding of human language, the following examples can highlight the versatility of this application.

• Social Media Monitoring: NLP based tools like Buffer can help businesses to monitor social media channels for mentions of their brand and alert them when consumers are talking about those brand.
• Text Classification: Wherein text inputs can be sorted by topic or tonality, and so on. This application supposedly aids in filtering spam emails, language identification, sentiment analysis (of product reviews), and type characterization (of books, motion pictures, arts, and so on).
• Sentiment analysis: NLP tools can be leveraged to measure customer opinions, monitor a company's reputation, and generally understand whether customers are happy with a product or service. This helps determine if a customer has a positive, negative, or neutral opinion towards a brand.
• Target Advertising: It is a type of online advertising where ads are shown to the user based on their online activity. Most of the online companies today use this approach because first, it saves companies much money, and second, relevant ads are shown only to the potential customers. NLP helps in target advertising using a technique called keyword matching.
• Email Categorization: Gmail uses NLP to group the emails we receive. We must have seen emails that are categorized into three tabs — Primary, Social, and Promotions. All the personal emails go into Primary, notifications from social media platforms go into Social, and newsletters from companies that user has signed up to hear from land in Promotions. Although it is not totally perfect, NLP helps identify and evaluate the content within each email, so that it can categorize them accurately.
• Automatic summarization: While NLP is known to help into autocorrect suggestions, it can also help summarize long documents into short, fluent, and accurate summaries. This particularly helpful in extracting keywords from a text. E.g., MeaningCloud, ML Analyzer
• Early Prediction of diseases: NLP has enabled the recognition and Prediction of diseases based on electronic health records and the patient's own speech. This capability is being explored in health conditions that go from cardiovascular diseases to depression and even schizophrenia.