Essential NLP Concepts and Terminology Explained

Fundamental NLP Concepts

Parsing: The process of analyzing sentence structure using grammar rules to determine syntactic relationships between words.
N-gram: A contiguous sequence of N words used to predict next-word probabilities in language models.
Cohesion vs. Coherence: Cohesion refers to the grammatical linking of words, while coherence refers to the logical and meaningful connection across sentences.
Smoothing: A technique used to adjust probabilities in language models to handle unseen words or zero-frequency problems.
Context-Free Grammar (CFG): A set of recursive production rules used to generate valid sentence structures independent of contextual information.
Evaluation Metrics: Perplexity is a common metric that measures how well a language model predicts a sequence of words.
Word Sense Disambiguation (WSD): The process of determining the correct meaning of a word based on its context in a sentence.
Parsing Strategies: Methods like top-down and bottom-up approaches used to analyze sentence structure using grammar rules.
Semantics: The study of meaning in language, focusing on the interpretation of words, phrases, and sentences in context.
First-Order Logic: A formal system using predicates, variables, and quantifiers to represent and reason about objects.

Lexeme: The base or dictionary form of a word representing its core meaning across different inflected forms.
Morpheme: The smallest meaningful unit in a language that cannot be further divided without losing meaning.
Treebank: A parsed text corpus used to train and evaluate syntactic parsers in natural language processing tasks.
N-Gram Model: A model that predicts the probability of a word based on the previous N-1 words in a sequence.
Ambiguity: Occurs when a word, phrase, or sentence has multiple possible meanings depending on context or interpretation.
Lemmatization: The process of reducing words to their base or dictionary form using vocabulary and morphological analysis.
Stop Word Removal: The process of eliminating common words like “the” and “is” to improve text processing efficiency.
Tokenization: The process of breaking text into smaller units such as words, phrases, or symbols for analysis.
Stemming: The process of reducing words to their root form by removing suffixes without considering linguistic correctness.
Sentiment Analysis: The process of identifying and classifying opinions expressed in text as positive, negative, or neutral.