Natural Language Processing NLP: What it is and why it matters

Instead of relying on computer language syntax, NLU enables a computer to comprehend and respond to human-written text. The letters directly above the single words show the parts of speech for each word (noun, verb and determiner). For example, “the thief” is a noun phrase, “robbed the apartment” is a verb phrase and when put together the two phrases form a sentence, which is marked one level higher. Syntax describes how a language’s words and phrases arrange to form sentences. For example, NLP allows speech recognition to capture spoken language in real-time, transcribe it, and return text- NLU goes an extra step to determine a user’s intent.

Nevertheless, thanks to the advances in disciplines like machine learning a big revolution is going on regarding this topic. Nowadays it is no longer about trying to interpret a text or speech based on its keywords (the old fashioned mechanical way), but about understanding the meaning behind those words (the cognitive way). This way it is possible to detect figures of speech like irony, or even perform sentiment analysis. Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications. In finance, NLP can be paired with machine learning to generate financial reports based on invoices, statements and other documents.

Supplementary Data 1

Sentiment analysis is widely applied to reviews, surveys, documents and much more. Semantics describe the meaning of words, phrases, sentences, and paragraphs. Semantic analysis attempts to understand the literal meaning of individual language selections, not syntactic correctness. However, a semantic analysis doesn’t check language data before and after a selection to clarify its meaning. This could be a binary classification (positive/negative), a multi-class classification (happy, sad, angry, etc.), or a scale (rating from 1 to 10). Put in simple terms, these algorithms are like dictionaries that allow machines to make sense of what people are saying without having to understand the intricacies of human language.

Topic Modeling is a type of natural language processing in which we try to find “abstract subjects” that can be used to define a text set. This implies that we have a corpus of texts and are attempting to uncover word and phrase trends that will aid us in organizing and categorizing the documents into “themes.” Natural language processing goes hand in hand with text analytics, which counts, groups and categorizes words to extract structure and meaning from large volumes of content. Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods.

Syntactic analysis

First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations natural language understanding algorithms within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing.

It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do.