15 Natural Language Processing Examples: NLP Applications

Machine learning’s ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery. Many of today’s leading companies, including Facebook, Google and Uber, make machine learning a central part of their operations. Still, most organizations either directly or indirectly through ML-infused products are embracing machine learning. Companies that have adopted it reported using it to improve existing processes (67%), predict business performance and industry trends (60%) and reduce risk (53%). There are calls that are recorded for training purposes but in actuality, they are recorded to the database for an NLP system to learn and improve services in the future.

Therefore, for something like the sentence above, the word “can” has several semantic meanings. The second “can” at the end of the sentence is used to represent a container. Giving the word a specific meaning allows the program to handle it correctly in both semantic and syntactic analysis. Hence, from the examples above, we can see that language processing is not “deterministic” (the same language has the same interpretations), and something suitable to one person might not be suitable to another.

Uses for Natural Language Processing in Healthcare

This tokenizer incorporates a variety of common rules for english word tokenization. It separates phrase-terminating punctuation like (?!.;,) from adjacent tokens and retains decimal numbers as a single token. You can easily tokenize the sentences and words of the text with the tokenize module of NLTK. NLTK (Natural Language Toolkit) is an open-source Python library for Natural Language Processing.

Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate speech. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches.

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But if we try to lemmatize the same word running as a noun it won’t be converted. Notice that the keyword “winn” is not a regular word and “hi” changed the context of the entire sentence. Which is made up of Anti and ist as the inflectional forms and national as the morpheme. Normalization is the process of converting a token into its base form. In the normalization process, the inflection from a word is removed so that the base form can be obtained.

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