Intro to Natural Language Processing (Grade A)

Summary:

Natural Language Processing (NLP) is a subset of artificial intelligence that equips machines to interpret and understand human languages. By merging linguistics and computer science, NLP models can analyze and extract pertinent information from both text and speech. Daily human interactions via social media produce a wealth of data that offers insights into human behaviour. This data is a goldmine for data analysts and machine learning experts, enabling machines to emulate human linguistic behaviour, leading to immense savings in time and resources.

NLP’s applications are more ubiquitous than often recognized. For instance, autocorrect aids in spelling, while plagiarism checkers scour the web for potential copyright infringements. While NLP might appear intricate, its foundational techniques are akin to teaching a child to read. The first steps involve segmentation (breaking a document into sentences) and tokenization (splitting sentences into words). To streamline this, nonessential “stop words” like “are” and “the” are often removed.

Understanding further requires recognizing the core essence of words, which involves processes like stemming (finding the root form of words) and lemmatization (identifying base words across tense, mood, etc.). Additionally, part-of-speech tagging labels words as nouns, verbs, etc., and named entity tagging identifies references to specific cultural or public figures, places, or events. By employing algorithms like Naive Bayes, machines are then trained to understand the sentiment. In essence, many NLP techniques reflect basic grammar concepts learned in school, with the most prominent ones being stemming, tokenization, lemmatization, and segmentation.

Excerpt:

Intro to Natural Language Processing

Natural Language Processing, or NLP, refers to the branch of artificial intelligence that allows machines to read, understand, and derive meaning from human languages. NLP combines the field of linguistics and computer science to decipher language structure and guidelines and make models that can comprehend, analyze, and extract significant details from text and speech.

Every day, humans interact through public social media, transferring vast quantities of freely available data to each other. This data is extremely useful in understanding human behaviour and customer habits.