What is natural language processing with examples?

Watson Oncology analyzes a patient’s medical records and pertinent data using natural language processing, assisting doctors in choosing the most appropriate course of therapy. It finds possible new applications for already-approved medications, accelerating the development of new drugs by evaluating vast amounts of scientific literature and research articles. NLP is more than simply teaching computers to comprehend human language. It also concerns their adaptability, dynamic, and capability, mirroring human communication. Understanding these fundamental ideas helps us better recognize how this contemporary technology fits into business processes and provides a platform for further investigation of its potential and valuable uses. In addition, NLP uses topic segmentation and named entity recognition (NER) to separate the information into digestible chunks and identify critical components in the text.

Once that’s done, a translation tool can generate a more accurate result in another language. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up. Online translators are now powerful tools thanks to Natural Language Processing. If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations. It couldn’t be trusted to translate whole sentences, let alone texts.

Reinforcement Learning

Smart assistants, which were once in the realm of science fiction, are now commonplace. Search autocomplete is a good example of NLP at work in a search engine. This function predicts what you might be searching for, so you can simply click on it and save yourself the hassle of typing it out. IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP. Syntactic Ambiguity exists in the presence of two or more possible meanings within the sentence. Dependency Parsing is used to find that how all the words in the sentence are related to each other.

An NLP system can look for stopwords (small function words such as the, at, in) in a text, and compare with a list of known stopwords for many languages. The language with the most stopwords in the unknown text is identified as the language. So a document with many occurrences of le and la is likely to be French, for example. When companies have large amounts of text documents (imagine a law firm’s case load, or regulatory documents in a pharma company), it can be tricky to get insights out of it.

Difference between Natural language and Computer Language

A Corpus is defined as a collection of text documents for example a data set containing news is a corpus or the tweets containing Twitter data is a corpus. So corpus consists of documents, documents comprise paragraphs, paragraphs comprise sentences and sentences comprise further smaller units which are called Tokens. When you use a concordance, you can see each time a word is used, along with its immediate context.

A major drawback of statistical methods is that they require elaborate feature engineering. Since 2015,[21] the statistical approach was replaced by neural networks approach, using word embeddings to capture semantic properties of words. For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurately translate text. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web.

This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It is used to group different inflected forms of the word, called Lemma. The main difference between Stemming and lemmatization is that it produces the root word, which has a meaning. For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word «intelligen.» In English, the word «intelligen» do not have any meaning.

As we mentioned before, we can use any shape or image to form a word cloud. Notice that we still have many words that are not very useful in the analysis of our text file sample, such as “and,” “but,” “so,” and others. Next, we can see the entire text of our data is represented as words and also notice that the total number of words here is 144. By tokenizing the text with sent_tokenize( ), we can get the text as sentences. Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023.

Examples of Natural Language Processing

NLP first rose to prominence as the backbone of machine translation and is considered one of the most important applications of NLP. Text extraction also has a variety of uses that can help IT and business professionals alike. Text extraction can be used to scan for specific identifying information across customer communications or support tickets, making it easier to route requests or search for select incidences. Have you ever texted someone and had autocorrect kick in to change a misspelled word before you hit send?

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