Part-1: Introduction to Natural Language Processing (NLP)


Introduction to NLP

What is Natural Language Processing (NLP)?

Natural language processing (NLP) is a field of artificial intelligence in which computers analyze, understand, and derive meaning information from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

NLP is characterized as a difficult problem in computer science. Human language is rarely precise or plainly spoken. To understand human language is to understand not only the words but the concepts and how they’re linked together to create meaning. Despite the language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language processing a difficult problem for computers to master.

Open Source NLP Libraries

These libraries provide the algorithmic building blocks of NLP in real-world applications. Algorithmia provides a free API endpoint for many of these algorithms, without ever having to set up or provision servers and infrastructure.

Applications of NLP:

Application of NLP

Embracing NLP in your business is a lot easier once you understand the tangible benefits instead of the abstract potential. With that in mind, here are a few ways NLP can elevate your bottom line over the next few years:

Optimize customer service: NLP optimizes customer service in two ways. First, it lets companies elevate their service levels by providing answers faster (through the web, chatbots, or voice applications like smart speaker apps), working in multiple languages, and handling higher-level questions. At the same time, NLP allows companies to spend less on human service agents, office spaces, phones, and other costs. With NLP, delivering exceptional service has never been easier or more affordable.

  • Improve regulatory compliance: NLP will transform administration in countless ways because machines can now complete work that previously required human eyes. For instance, this is tremendously helpful with regulatory compliance. Instead of asking compliance officers to pore over oceans of data looking for potential violations, computers can automate the initial review and escalate potential irregularities to compliance officers. Better yet, this approach takes less time and leads to fewer mistakes.
  • Learn from customer data: Customers leave data everywhere — think online reviews, Facebook posts, direct emails, and more. Thanks to NLP, companies can easily process this wealth of information and use it to evaluate customer sentiments. More broadly, NLP lets companies glean more insights from customer data regardless of its source, size, or format.
  • Augment text-intensive tasks: The first pass for a number of text-intensive tasks can be automated using NLP-based applications. This has already been successfully applied to spam detection in emails, but it has numerous applications within enterprises. For example, HR professionals and recruiters could reduce their workloads by intelligently sorting and categorizing heaps of résumés via automation.
  • Search beyond keywords: Most businesses rely on search technologies that are antiquated and based on keyword matching. This produces less-than-optimal results for customers, employees, and partners. Applying NLP to search produces results based on an understanding of the meaning of the query instead of just by matching the keywords.

Why Natural Language Processing?

Large volumes of textual data:

  • Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read the text, hear speech, interpret it, measure sentiment, and determine which parts are important.
  • 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. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter, and borrow terms from other languages.

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.

Our Real-Life Examples:

  • Have you ever looked at the emails in your spam folder and noticed similarities in the subject lines? You’re seeing Bayesian spam filtering, a statistical NLP technique that compares the words in spam to valid emails to identify junk mail.
  • Have you ever missed a phone call and read the automatic transcript of the voicemail in your email inbox or smartphone app? That’s speech-to-text conversion, an NLP capability.
  • Have you ever navigated a website by using its built-in search bar, or by selecting the suggested topic, entity, or category tags? Then you’ve used NLP methods for search, topic modeling, entity extraction, and content categorization.

Feel free to use our github repo associated with this NLP series :

Quick Links

Link : Part -2 : Tokenization

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