The term “Natural Language Processing” (NLP) dispels any misconceptions about how it relates to language or linguistics. The two main features of NLP are “Human to Machine Translation” (also known as “Natural Language Understanding”) and “Machine to Human Translation” (also known as “Natural Language Generation”).
The field of Natural Language Processing (NLP) combines linguistics, computer science, and artificial intelligence. The ultimate aim of this technology is to enable computers to comprehend the sentiment, nuance, and content of documents.
With the help of NLP, we can precisely extract the knowledge and insights from the document and classify them into the appropriate groups. For instance, whenever a user searches for something on the Google search engine, Google’s algorithm uses NLP techniques to display all the pertinent documents, blogs, and articles.
NLP dates back to 1950, when Alan Turing released an article titled “Computing Machinery and Intelligence” (often referred to as the “Turing test”) that is credited with launching the field. In that article, the topic “Can machines think?” was taken into consideration because it contained two ambiguous words: “machines” and “think.” The Turing test proposed a few adjustments, replacing the original question with one that was closely related and phrased in clear terms.
The work of Chomsky and others on generative syntax and formal language theory led to the development of various natural language processing systems in 1960, including SHRDLU. The evolution began in natural language processing throughout the 1980s with the introduction of machine learning language processing techniques. A significant amount of audio and text material was later released in 2000, and the data was available for everyone.
Natural Language Processing Techniques
NER – Named Entry Recognition
This method is one of the most well-liked and useful methods in semantic analysis. Semantics is a concept that the text conveys. In this method, the algorithm reads a sentence or a paragraph and determines all the nouns or names that are there.
Tokenization is the process of breaking down a text into a list of tokens, which might include everything from words to phrases to characters to numbers to punctuation. Tokenization has two key benefits: first, it significantly reduces search, and second, it makes efficient use of storage space.
The initial phases of any NLP challenge involve mapping sentences from characters to strings and strings to words since, to understand any text or document, we must interpret the words and sentences that are present in it.
Any Information Retrieval (IR) system must include tokenization because it not only requires text pre-processing but also produces tokens that are employed in the indexing and ranking procedures. There are many other tokenization methods available, however, Porter’s Algorithm is one of the more well-known methods.
Stemming and Lemmatisation
The size of data and information on the internet has increased to an all-time high in the last few years. To easily draw conclusions from this vast amount of data and information, it is vital to use the right tools and methodologies.
Stemming is the process of reducing inflected (or occasionally derived) words to their printed word stem, base, or root form. For instance, stemming basically removes all the suffixes.
Lemmatization typically refers to actions taken with the appropriate use of vocabulary and morphological analysis of words, usually with the goal of removing only inflectional endings and returning the lemma, or dictionary form, of a word. Lemmatization, to put it simply, is the process of lowering a word’s form after recognising its part of speech (POS) or context in a piece of writing.
Bag of Words
For use in machine learning modelling, the bag of words technique is used to pre-process text and extract all the features from a text source. It also serves as a representation of any text that clarifies or elucidates how the words are used in a corpus (document). It is also known as “Bag” due to the way it works, which is to say that it merely looks to see if recognised terms appear in the document, not where they are.
Natural Language Generation
Natural language generation (NLG) is a method that transforms unprocessed structured data into plain English (or any other language) using machine learning. Data storytelling is another name for it. This method, which transforms structured data into plain languages for a better understanding of patterns or in-depth insights into any business, is highly useful in many organisations where a big volume of data is utilised.
This contrasts with Natural Language Understanding (NLU), which we have already discussed. By producing reports that are mostly data-driven, such as stock market and financial reports, meeting notes, reports on product requirements, etc., NLG makes data understandable to everyone.
It is one of the most widely used methods for natural language processing. We can comprehend the emotion/feeling of the textual text by using sentiment analysis. Emotion AI and opinion mining are other names for sentiment analysis.
Finding the polarity of text is another name for the core task of sentiment analysis, which is to determine if the opinions represented in any sentence, text, social media post, or film review are positive, negative, or neutral.
In most cases, subjective text data works better for sentiment analysis than objective test data. Typically, objective text data consists of assertions or facts that are devoid of all sentiment. Contrarily, subjective language is typically created by people who express their emotions and sentiments.
This technique’s most basic duty is to break up all text into intelligible sentences or phrases. This assignment entails locating the boundaries of sentences inside text texts. Sentence segmentation is also known as sentence boundary detection, sentence boundary disambiguation, or sentence boundary recognition since punctuation marks are often presented at sentence boundaries in almost all languages.