Examples of Natural Language Processing. Based on artificial intelligence algorithms and driven by an increased need to manage unstructured enterprise information along with structured data, Natural Language Processing (NLP) is influencing a rapid acceptance of more intelligent solutions in various end‐use applications. In this post, we’ll look at a few natural language processing techniques.
Whether it’s referred to as computational linguistics or text mining the goal for natural language processing is the same: to process everyday language and turn spoken words–text or speech–into structured data. To be able to analyze language for its meaning is a complex task. Technologies that treat language as anything but language—such as a sequence of symbols in pattern matching, or based on the distribution and frequency of keywords and co‐occurrences as with statistics methodologies, or even on language patterns as with shallow analysis (such as deep learning)—still seem to be a long way from achieving this goal. NLP algorithms that do not have an authentic comprehension of language will always be limited in their language understanding capabilities.
Cognitive computing attempts to overcome these limits by applying semantic algorithms that mimic the human ability to read and understand.
Natural Language Processing Techniques in the Enterprise Data World
Natural language processing is largely applied in a variety of industry segments (media, publishing, advertising, healthcare, banking and insurance) to improve important enterprise activities, including:
Formulating responses to questions. Enterprise question answering tools leverage NLP algorithms to enhance customer experience and improve administrative activities by allowing users to ask questions in everyday language about products, services or applications and receive immediate and accurate answers. Virtual assistants (or virtual agents), for example, simulate a conversation with users to optimize customer support activities.
Social media monitoring. Social media monitoring represents a great opportunity for companies to know what their clients are talking about on social media platforms, blogs, etc. and to discover relevant information for their business. By interacting with clients, processing their conversations and essentially understanding customers in their own words, companies can better understand their customers’ needs and improve the relationships with them.
Text analytics. Many organizations leverage natural language processing to approach text problems and improve activities such as knowledge management and big data analytics. Morphological, grammatical, syntactic and semantic analyses of language enable identification and extraction of different types of key elements (topics, locations, people, companies, dates, etc.), and generate the metadata that can be used to tag and categorize content in the most precise way.
Conclusion
Natural language processing is a form of artificial intelligence that helps computers read and respond by simulating the human ability to understand everyday language. Many organizations use NLP techniques to optimize customer support, improve the efficiency of text analytics by easily finding the information they need, and enhance social media monitoring. For example, banks might implement NLP algorithms to optimize customer support; a large consumer products brand might combine natural language processing and semantic analysis to improve their knowledge management strategies and social media monitoring.
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