Natural Language Processing (NLP) Defined
NLP is an AI branch enabling computers to understand human language. It’s used in virtual assistants like Siri and Alexa. NLP applies to written text and speech in all human languages. It’s also used in web search, email spam filtering, translation, and sentiment analysis. NLG and NLU are similar and refer to computers producing and understanding human language. Computational linguistics studies computational aspects of human language while NLP builds computational artifacts that manipulate human language. NLP draws on both linguistics and AI. Machine learning powers NLP, particularly deep learning which can learn complex patterns from large datasets, suited to natural language complexities.
Applications of Natural Language Processing
Automate routine tasks: Chatbots are digital systems that use NLP to handle routine tasks and free up human employees for more engaging tasks. They recognize user requests, match them with the appropriate database entry and provide relevant responses. Chatbots are used in various industries, improving customer service and organizational efficiency.
Improve search: NLP helps improve search queries by disambiguating word senses and matching synonyms. It is useful for non-English queries and academic research retrieval. By providing more precise and relevant information, it makes it easier for doctors, lawyers, and other specialists to make better decisions.
Search engine optimization: Utilizing NLP can boost a business’s online presence. By integrating NLP techniques, businesses can improve their search engine rankings, attract more customers, and increase revenue and profit. Understanding and leveraging NLP technology can make your content more search engine friendly, resulting in greater visibility in the online domain. In summary, incorporating NLP into your online strategy can lead to positive results.
Analyzing and organizing large document collections: NLP techniques such as document clustering and topic modeling simplify the task of understanding the diversity of content in large document collections, such as corporate reports, news articles, or scientific documents. These techniques are often used in legal discovery purposes.
Social media analytics: NLP provides valuable insights for businesses by analyzing large amounts of customer reviews and social media comments. Sentiment analysis, a technique used in NLP, distinguishes between positive and negative comments, providing real-time feedback for businesses to improve customer satisfaction, make informed decisions and increase revenue. This strong connection enables businesses to grow their brand and gain market share.
Market insights: NLP and aspect-oriented sentiment analysis can help you understand your customers’ needs and preferences. Analyzing the language used by your customers provides deep insights into what they want and how to engage with them in a meaningful way. With this data, you can make data-driven decisions about product design and marketing strategies, positioning your business for success.
Moderating content: If your business attracts large amounts of user or customer comments, NLP enables you to moderate what’s being said in order to maintain quality and civility by analyzing not only the words, but also the tone and intent of comments.
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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.
NLP Technology Overview
Machine learning models for NLP: Modern NLP uses machine learning to make predictions by generalizing over examples in a dataset. The algorithms train on a training dataset to produce a model that performs a target task. Sentiment analysis uses a document classification model that reads sentences and produces sentiments, while entity recognition uses a sequence labeling model to recognize and classify entities in documents by labeling each word in the input.
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