Every day, a large number of people interact with various chatbots and virtual assistants. We ask questions that matter to us, and in most cases, they understand us perfectly and solve our problems. However, few people stop to think about how such AI solutions manage to achieve this. Natural Language Processing (NLP) is one of the key components of AI in this industry, significantly expanding its capabilities and enabling it to effectively perform various user functions.
Natural Language Processing From Data To Insight
Nature language processing utilizes machine learning, which ensures that computers can accurately and efficiently understand and communicate in human language. In addition to machine learning, NLP also involves processes such as statistical modeling and deep learning.
The core of this technology is based on combining various computational methods, allowing machines to analyze, understand, and generate human language. There are two key stages in natural language processing: data preprocessing and algorithm creation.
First, the NLP process begins with data preprocessing. Raw text is prepared for analysis by converting it into a format that is easier for machines to read, breaking it down into smaller units. Uppercase letters and insignificant words—such as “the”—which do not add meaning to the text, are also removed. The result is a standardized text ready for further analysis.
The second stage is called feature extraction. Here, the text is converted into numerical representations, which are then analyzed and interpreted by machines.
In the third step, computational methods are used to interpret the text, focusing on extracting meaningful information. This step involves Part of Speech (POS) tagging, which relates to word grammar, and Named Entity Recognition (NER), which identifies specific information about an entity.
At this stage, dependency and sentiment analyses are incorporated. In the first case, sentence structure is identified, while in the second, emotional tone is determined. Lemmatization merges different inflected forms of the same word, and words are tagged according to their parts of speech. The key themes of the text are also identified.
In the data processing stage, algorithms are created. A rule-based system can be used, which involves linguistic rules, or a machine learning-based system, which uses statistical data, reducing errors and improving model performance. Various software environments can be used throughout the process. For example, the Natural Language Toolkit (NLTK) written in Python is commonly used.
Natural Language Processing Applications
This technology can be applied in various tasks. For example, text classification helps find the number of mentions of a specific object, and text extraction gathers specific keywords. NLP also opens up possibilities for text translation or generating content.
All of this enables people and technology to collaborate, improving various processes, such as automating repetitive tasks for organizations, analyzing accumulated databases, enhancing search processes, and generating content. Certain biometric time and attendance software can be used in work organization processes.
Of course, as the technology continues to develop, there are still challenges in understanding human queries and possible ambiguities or nuances of language in texts, such as sarcasm, which can lead to less accurate solutions.
NLP is integrated across various fields. For example, NetGeist’s natural language processing solutions include chatbots, which are integrated into sectors such as ecommerce and healthcare, improving customer shopping experiences by answering queries or assisting medical staff with manual tasks.
Another example of an NLP-integrated product is StockGeist – a market sentiment monitoring platform that is used to analyze the sentiment of the public, investors, and text messages on social media to assess their impact on stock prices and market value.
Final Word
Natural language processing demonstrates that the collaboration between humans and machines is becoming a daily reality. It is becoming an essential part of every business and a crucial investment in both internal and external processes, enhancing the company’s productivity and improving the experience of the target audience.
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Sources: IBM, NetGeist, TechTarget
