Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing has the ability to interrogate the data with natural language text or voice. This is also called “language in.” Most consumers have probably interacted with NLP without realizing it. For instance, NLP is the core technology behind virtual assistants, such as the Oracle Digital Assistant (ODA), Siri, Cortana, or Alexa. When we ask questions of these virtual assistants, NLP is what enables them to not only understand the user’s request, but to also respond in natural language. NLP applies both to written text and speech, and can be applied to all human languages. Other examples of tools powered by NLP include web search, email spam filtering, automatic translation of text or speech, document summarization, sentiment analysis, and grammar/spell checking. For example, some email programs can automatically suggest an appropriate reply to a message based on its content—these programs use NLP to read, analyze, and respond to your message.
In practice, NLU (Natural Language Understanding) is used to mean NLP. The understanding by computers of the structure and meaning of all human languages, allowing developers and users to interact with computers using natural sentences and communication. Computational linguistics (CL) is the scientific field that studies computational aspects of human language, while NLP is the engineering discipline concerned with building computational artifacts that understand, generate, or manipulate human language.
Research on NLP began shortly after the invention of digital computers in the 1950s, and NLP draws on both linguistics and AI. However, the major breakthroughs of the past few years have been powered by machine learning, which is a branch of AI that develops systems that learn and generalize from data. Deep learning is a kind of machine learning that can learn very complex patterns from large datasets, which means that it is ideally suited to learning the complexities of natural language from datasets sourced from the web.
What Is NLP Used For
Automate routine tasks: Chatbots powered by NLP can process a large number of routine tasks that are handled by human agents today, freeing up employees to work on more challenging and interesting tasks. For example, chatbots and Digital Assistants can recognize a wide variety of user requests, match them to the appropriate entry in a corporate database, and formulate an appropriate response to the user.
Improve search: NLP can improve on keyword matching search for document and FAQ retrieval by disambiguating word senses based on context (for example, “carrier” means something different in biomedical and industrial contexts), matching synonyms (for example, retrieving documents mentioning “car” given a search for “automobile”), and taking morphological variation into account (which is important for non-English queries). Effective NLP-powered academic search systems can dramatically improve access to relevant cutting-edge research for doctors, lawyers, and other specialists.
Search engine optimization: NLP is a great tool for getting your business ranked higher in online search by analyzing searches to optimize your content. Search engines use NLP to rank their results—and knowing how to effectively use these techniques makes it easier to be ranked above your competitors. This will lead to greater visibility for your business.
Analyzing and organizing large document collections: NLP techniques such as document clustering and topic modeling simplify the task of understanding the diversity of content in large document collections, such as corporate reports, news articles, or scientific documents. These techniques are often used in legal discovery purposes.
Social media analytics: NLP can analyze customer reviews and social media comments to make better sense of huge volumes of information. Sentiment analysis identifies positive and negative comments in a stream of social-media comments, providing a direct measure of customer sentiment in real time. This can lead to huge payoffs down the line, such as increased customer satisfaction and revenue.
Market insights: With NLP working to analyze the language of your business’ customers, you’ll have a better handle on what they want, and also a better idea of how to communicate with them. Aspect-oriented sentiment analysis detects the sentiment associated with specific aspects or products in social media (for example, “the keyboard is great, but the screen is too dim”), providing directly actionable information for product design and marketing.
Moderating content: If your business attracts large amounts of user or customer comments, NLP enables you to moderate what’s being said in order to maintain quality and civility by analyzing not only the words, but also the tone and intent of comments.
What did we do in NLP?
At Proxima, we have developed Chatbots, NLP interface to large public utility data sets, NLP interface to remote video end point bug reports etc. Reach out to us if you are looking for expertise in this area.