Natural Language Processing: Enhancing Communication with AI Systems
Tags: talentserve, ai, nlp, industries
Published: 2024-05-14 11:28:47

**NLP Technology Overview**
**Machine learning models for natural language processing (NLP):** As we previously discussed, machine learning is a major AI technique utilized in current NLP. By drawing generalizations from instances in a dataset, machine learning generates predictions. Machine learning algorithms train on this dataset, referred to as the training data, in order to create a machine learning model that successfully completes a task.

Sentences with their corresponding sentiment, such as positive, negative, or neutral, comprise sentiment analysis training data, for instance. This dataset is read by a machine learning algorithm, which then creates a model that accepts sentences as input and returns the attitudes associated with them. A document classification model is a type of model that receives sentences or documents as inputs and outputs a label for each input. 

Entities in documents are identified and categorized using a different type of model. The model predicts whether a term in a document refers to an entity and, if so, what kind of thing is mentioned. For instance, "XYZ Corp" is the name of the corporation, "$28" is the amount in currency, and "yesterday" is the date in "XYZ Corp shares traded for $28 yesterday." A set of texts is used as the training data for entity recognition, with each word labeled with the kind of entities it relates to. Sequence labeling models are the ones that generate a label for every word in the input.

The family of models used in NLP has very recently included sequence to sequence models. As with a document classifier, a sequence to sequence (or seq2seq) model accepts an entire sentence or document as input and outputs a sentence or another sequence (like a computer program). The output of a document classifier is limited to a single symbol. Sequence-to-sequence models are used in a variety of applications, such as machine translation (which, for instance, converts an English sentence into its French equivalent); document summarization (which produces an output that is an overview of the input); and semantic parsing (which takes an English query or request as input and outputs a computer program that carries out that request).

**Transfer learning, deep learning, and pretrained models:** In NLP, deep learning is the most popular type of machine learning. By drawing an analogy with brains, researchers created neural networks in the 1980s, which are made up of several basic machine learning models linked into a single network. These basic models are frequently referred to as "neurons." Layers comprise these neurons, and a deep neural network has numerous layers. Machine learning with deep neural network models is called deep learning.

**Industries Using Natural Language Processing**

NLP simplifies and automates a wide range of business processes, especially ones that involve large amounts of unstructured text like emails, surveys, social media conversations, and more. With NLP, businesses are better able to analyze their data to help make the right decisions. Here are just a few examples of practical applications of NLP:

**Healthcare:** As healthcare systems all over the world move to electronic medical records, they are encountering large amounts of unstructured data. NLP can be used to analyze and gain new insights into health records.
**Legal:** To prepare for a case, lawyers must often spend hours examining large collections of documents and searching for material relevant to a specific case. NLP technology can automate the process of legal discovery, cutting down on both time and human error by sifting through large volumes of documents.
**Finance:** The financial world moves extremely fast, and any competitive advantage is important. In the financial field, traders use NLP technology to automatically mine information from corporate documents and news releases to extract information relevant to their portfolios and trading decisions.
**Customer service:** Many large companies are using virtual assistants or chatbots to help answer basic customer inquiries and information requests (such as FAQs), passing on complex questions to humans when necessary.
**Insurance:** Large insurance companies are using NLP to sift through documents and reports related to claims, in an effort to streamline the way business gets done.

 In summary, The field of natural language processing is a dynamic one in artificial intelligence, fostering the creation of numerous innovative technologies like chatbots, search engines, recommendation engines, and speech-to-text systems. Natural language processing will continue to be in high demand as computer-human interfaces continue to diverge from buttons, forms, and domain-specific languages. Because of this, Oracle Cloud Infrastructure is dedicated to offering on-premises performance with our NLP tools and compute architectures that are optimized for performance. To start exploring with NLP, Oracle Cloud Infrastructure provides a variety of GPU shapes that you can deploy in a matter of minutes.