Distributional semantics
Research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in language data
Distributional semantics is a subfield of natural language processing (NLP) that deals with the study of word meaning in context. It is based on the idea that the meaning of a word can be inferred from the way it is used in a text or a corpus (a large collection of texts).
In distributional semantics, words that are used in similar contexts are assumed to have similar meanings. For example, the words "cat" and "dog" are likely to be used in similar contexts, so they are likely to have similar meanings. This idea is known as the distributional hypothesis.
To apply distributional semantics, NLP systems analyze a large corpus of texts and build a representation of the meanings of words based on the contexts in which they are used. These representations, known as word embeddings, capture the meaning of a word in a numerical form that can be used by machine learning algorithms.
Distributional semantics has a wide range of applications in NLP, including word sense disambiguation, text classification, and machine translation. It is an important tool for understanding the meaning of words in natural language text and is a key component of many NLP systems.
Distributional hypothesis: linguistic items with similar distributions have similar meanings.
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