Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. NLP is the technology used to aid computers to understand the human’s natural language.The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages.
This can be a difficult task to achieve. Why? The rules that dictate the passing of information using natural languages are not easy for computers to understand. Some of these rules can be high-leveled and abstract; for example, when someone uses a sarcastic remark to pass information. On the other hand, some of these rules can be low-leveled; for example, using the character “s” to signify the plurality of items. Comprehensively understanding the human language requires understanding both the words and how the concepts are connected to deliver the intended message.
In NLP, syntactic analysis is used to assess how the natural language aligns with the grammatical rules. Computer algorithms are used to apply grammatical rules to a group of words and derive meaning from them.
Here are some techniques the NLP uses;
- Lemmatization: It entails reducing the various inflected forms of a word into a single form for easy analysis.
- Part-of-speech tagging: It involves identifying the part of speech for every word.
- Parsing: It involves undertaking grammatical analysis for the provided sentence.
- Stemming: It involves cutting the inflected words to their root form.
- Stopwords: set of commonly used words that does not change the meaning or context of a sentence or statement
Now, let’s explore some techniques for extracting information from a text.
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Named Entity Recognition: The most basic and useful technique in NLP is extracting the entities in the text. It highlights the fundamental concepts and references in the text. Named entity recognition (NER) identifies entities such as people, locations, organizations, dates, etc. from the text.
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Sentiment Analysis: The most widely used technique in NLP is sentiment analysis. Sentiment analysis is most useful in cases such as customer surveys, reviews and social media comments where people express their opinions and feedback. The simplest output of sentiment analysis is a 3-point scale: positive/negative/neutral. In more complex cases the output can be a numeric score that can be bucketed into as many categories as required.
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Text Summarization: As the name suggests, there are techniques in NLP that help summarize large chunks of text. Text summarization is mainly used in cases such as news articles and research articles.
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Aspect Mining: Aspect mining identifies the different aspects in the text. When used in conjunction with sentiment analysis, it extracts complete information from the text. One of the easiest methods of aspect mining is using part-of-speech tagging.
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Topic Modeling: Topic modeling is one of the more complicated methods to identify natural topics in the text. A prime advantage of topic modeling is that it is an unsupervised technique. Model training and a labeled training dataset are not required.
These are just a few techniques of natural language processing. Once the information is extracted from unstructured text using these methods, it can be directly consumed or used in clustering exercises and machine learning models to enhance their accuracy and performance.