Glossary

Welcome to the GraphoVerba glossary, your reference dictionary for navigating the fascinating world of graph theory, neural networks, and computational linguistics.

The goal of this page is simple: to demystify technical terminology. Here you'll find clear and concise definitions of the key concepts we mention in our articles. This is a living document; we will constantly update and expand it with new terms.

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A

Algorithm [Computing]

A set of well-defined and ordered instructions or rules that allow an activity to be performed in successive steps. In our context, these are the "recipes" we use to analyze graphs, train neural networks, or process text.

Edge [Graph Theory]

The line that connects two nodes (or vertices) in a graph. It represents the relationship or connection between them. For example, in a social network graph, an edge between two people means they are friends.

Artificial Neuron [Neural Networks]

The basic computational unit of a neural network. It receives one or more inputs, performs a mathematical operation (a weighted sum followed by an activation function), and produces an output.

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B

Backpropagation [Neural Networks]

The fundamental algorithm for training neural networks. It works by calculating the error in the network's output and "propagating" this error backward, adjusting the connection weights to minimize the error in the next iteration.

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C

Path [Graph Theory]

A sequence of nodes in which each adjacent node in the sequence is connected by an edge. A simple path is one that does not repeat nodes.

Corpus [Computational Linguistics]

A large set of texts, usually structured and collected for a specific purpose, used to train and evaluate language models. It is the "study material" for NLP algorithms.

Computational Linguistics [Linguistics]

An interdisciplinary field that deals with natural language modeling from a computational perspective. It is the science behind how machines can process, understand, and generate human language.

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D

Directed Graph (Digraph) [Graph Theory]

A type of graph where edges have a direction, often represented as arrows. The relationship between nodes is unidirectional (from A to B does not imply from B to A). An example is the Twitter network, where following someone does not guarantee that they will follow you back.

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E

Embedding (Vector Embedding or Representation) [Neural Networks]

A technique for representing words or other discrete elements as vectors of real numbers in a high-dimensional space. The major advantage is that words with similar meanings will have nearby vectors in this space, allowing models to capture semantic relationships.

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G

Graph [Graph Theory]

A mathematical structure composed of a set of objects called nodes (or vertices) and a set of edges that connect them. They are used to model relationships and networks in countless domains.

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L

Lemmatization [Computational Linguistics]

The process of reducing a word to its base or root form, known as a "lemma." Unlike stemming, lemmatization uses a dictionary and morphological analysis to ensure the lemma is a real word. Example: "walking" -> "walk."

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N

Node/Vertex [Graph Theory]

The fundamental entity in a graph that represents an object or data point. It can be a person, a city, a word in a text, or even another graph.

NLP (Natural Language Processing) [Computational Linguistics]

An area of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a valuable way.

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S

Semantics [Linguistics]

The branch of linguistics that studies the meaning of words, phrases, and sentences. In NLP, semantic analysis seeks to determine the meaning of a text.

Syntax [Linguistics]

The set of rules, principles, and processes that govern the structure of sentences in a language. Parsing seeks to identify the grammatical structure of a sentence.

Stemming [Computational Linguistics]

A fast, heuristic process that involves truncating the ends of words to obtain a common root or "stem." It does not always result in a real word. Example: "walking" -> "walking."

Stop Words [Computational Linguistics]

Very common words in a language (such as "the," "and," "a," "an") that are often removed from text before processing because they provide little semantic meaning and can introduce noise.

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T

Tokenization [Computational Linguistics]

The process of breaking down text into smaller units called "tokens." These tokens are typically words, numbers, or punctuation marks. It is one of the first steps in any NLP task.

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V

Vector [Computation]

In the context of NLP and neural networks, a vector is an ordered list of numbers that represents an object (such as a word or a document) in a mathematical space.

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W

Weighted Graph [Graph Theory]

A graph where each edge has an associated numerical value, called a "weight" or "cost." This weight can represent distance, time, capacity, or the strength of a relationship.