Natural Language Processing NLP: 7 Key Techniques

With the addition of more channels into the mix, the method of communication has also changed a little. Consumers today have learned to use voice search tools to complete a search task. Since the SEO that businesses base their marketing on depends on keywords, with voice-search, the keywords have also changed.

By tokenizing the text with word_tokenize( ), we can get the text as words. The NLTK Python framework is generally used as an education and research tool. However, it can be used to build exciting programs due to its ease of use.

Natural language processing tools

It helps the computer understand how words form meaningful relationships with each other. Many organizations are seeing the value of NLP, but none more than customer service. Customer service support centers and help desks are overloaded with requests. NLP systems aim to offload much of this work for routine and simple questions, leaving employees to focus on the more detailed and complicated tasks that require human interaction.

Text summarization is the breakdown of jargon, whether scientific, medical, technical or other, into its most basic terms using natural language processing in order to make it more understandable. Natural language processing is the artificial intelligence-driven process of making human input language decipherable to software. These are some of the basics for the exciting field of natural language processing (NLP). We hope you enjoyed reading this article and learned something new. In the graph above, notice that a period “.” is used nine times in our text.

Data analysis

Predictive text, autocorrect, and autocomplete have become so accurate in word processing programs, like MS Word and Google Docs, that they can make us feel like we need to go back to grammar school. You can even customize lists of stopwords to include words that you want to ignore. You can try different parsing algorithms and strategies depending on the nature of the text you intend to analyze, and the level of complexity you’d like to achieve. Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs. The idea is to group nouns with words that are in relation to them. Below is a parse tree for the sentence “The thief robbed the apartment.” Included is a description of the three different information types conveyed by the sentence.

NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language. With the use of sentiment analysis, for example, we may want to predict a customer’s opinion and attitude about a product based on a review they wrote. Sentiment analysis is widely applied to reviews, surveys, documents and much more. Let’s look at some of the most popular techniques used in natural language processing.

Lemmatization and Stemming

Think about words like “bat” (which can correspond to the animal or to the metal/wooden club used in baseball) or “bank” (corresponding to the financial institution or to the land alongside a body of water). By providing a part-of-speech parameter to a word ( whether it is a noun, a verb, and so on) it’s possible to define a role for that word in the sentence and remove disambiguation. Natural Language Processing or NLP is a field of Artificial Intelligence that gives the machines the ability to read, understand example of nlp and derive meaning from human languages. Honest customer feedback provides valuable data points for companies, but customers don’t often respond to surveys or give Net Promoter Score-type ratings. As such, conversational agents are being deployed with NLP to provide behavioral tracking and analysis and to make determinations on customer satisfaction or frustration with a product or service. Additionally, chatbots can be trained to learn industry language and answer industry-specific questions.