Natural Language Processing- A Hassle-free Way to Extract Information
[vc_row pix_particles_check=””][vc_column][vc_column_text]The human brain is an unbelievably complex structure that performs highly complex tasks effortlessly. The functioning of the brain is so intricate, that we will probably never be able to understand it completely. With Artificial Intelligence growing by leaps and bounds today, we are probably closer than we were before in enabling systems to mimic the human brain, at least in small parts. Language or Speech is one such complex process that the brain performs with ease. Integration of AI withĀ Natural Language Processing (NLP), mimicking this complex function of the brainĀ has become somewhat possible.
Talking about the benefits and applications of Natural Language Processing, it is emerging as a robust solution for data extraction or data interpretation these days. With rising amounts of unstructured data in the corporate world, the utilization of Natural Language Processing can transform operations completely. So, let us learnĀ what is Natural language processing information extractionĀ and why people think that it is the next big thing in the professional space.[/vc_column_text][/vc_column][/vc_row][vc_row pix_particles_check=””][vc_column][vc_column_text]
How Does NLP Information Extraction?
Natural Language Processing Information Extraction takes place with the help of the following techniques :
Named Entity Recognition
Natural Language Processing works on two basic processes, namely- Natural Language Understanding and Natural Language Generation. Natural Language Understanding is related to translation from the human to the machine form, while the Natural Language Generation process is related to the machine response given to the user.
Named Entity Recognition is a basic technique with which the system can recognize and extract entities within the text. Entities such as locations, names, organizations, people, etc., can easily be extracted using the Named Entity Recognition technique. To perform these actions, the Named Entity Recognition techniques utilize basic grammar rules and work under supervised models. Moreover, with Open Natural Language Processing platforms, built-in Named Entity Recognition models are also available.
Text Summarization
Text summarization is the second most widely used NLP technique for information extraction. It involves the breaking down and summarization of large amounts of data, especially the text present in newspapers or long-form articles or business documents. Text summarization works basically on two principles. The first is called Extraction, where the model extracts text from the document and creates a summary for every part it takes out from the source. The second is the Abstraction process, which involves the creation of new content that basically conveys the gist of the entire document from which the information is to be extracted.
Text summarization based on Natural Language Processing can be implemented by utilizing various kinds of algorithms. Itās a very useful method ofĀ natural language processing information extraction.
Sentiment Analysis
Operations that involve dealing with data such as customer reviews, social media comments, etc., can benefit greatly from the Sentiment Analysis technique of Natural Language Processing Information Extraction. The Sentiment Analysis technique is based on a three-point scale comprising positive, negative, and neutral parameters, respectively. As the name suggests, Sentiment Analysis helps in classifying reviews and comments, based on the āsentimentā which may either be praiseworthy or might involve a complaint or negative feedback.
Furthermore, Sentiment Analysis can be implemented using supervised as well as unsupervised techniques. For theĀ supervised Sentiment Analysis technique, a model has to be trained with specific sentiment labels so that it can identify the same when it encounters it in real-time. When a general corpus of words is used with their sentiments and specific polarity for this procedure, it is called Unsupervised Sentiment Analysis. Both of these methods are available for NLP information extraction python.
Aspect Mining
For fruitful information extraction, the assessment of sentiments is not enough. The aspects and context of the text also need to be understood very accurately. To perform this function, Natural Language Processing platforms use a technique called Aspect Mining. Aspect Mining and Sentiment Analysis are used together more often than not because in conjunction, they convey the total meaning of the source text. Part-of-speech tagging is the most widely used method of Aspect Mining. The process of part-of-speech tagging can be compared to understanding the English language through its aspects like nouns, verbs, pronouns, etc.
Topic Modeling
Topic Modelling is a complex and advanced technique with whichĀ NLP extracts information from text. It primarily involves the discovery and understanding of abstract concepts that are usually present in documents. In simple words, Topic Modelling helps in identifying the various ātopicsā that a particular document is based upon. This becomes possible by identifying a cluster of words that appear repeatedly. The more is the repetition of a particular word, the more is its importance, and thus higher are the chances that the entire document majorly revolves around that particular word.
KlearStack NLP Information Extraction Solutions
KlearStackĀ provides Artificial Intelligence-backedĀ data extraction solutions that not only retrieve the text from images, documents, etc. but also manage to interpret the data from unstructured documents with excellent precision.
Our solutions are based on Natural Language Processing methodologies, and utilize all common and industry-relevant techniques for information extraction. Once the information has been extracted, our machine learning models process and polish them further to ensure that all the errors are rectified and the end-user receives only highly accurate results.
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