Search results
Results from the WOW.Com Content Network
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
Semantics studies meaning in language, which is limited to the meaning of linguistic expressions. It concerns how signs are interpreted and what information they contain. An example is the meaning of words provided in dictionary definitions by giving synonymous expressions or paraphrases, like defining the meaning of the term ram as adult male sheep. [22]
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.
Conceptual semantics is a framework for semantic analysis developed mainly by Ray Jackendoff in 1976. Its aim is to provide a characterization of the conceptual elements by which a person understands words and sentences, and thus to provide an explanatory semantic representation (title of a Jackendoff 1976 paper).
Lexical semantics (also known as lexicosemantics), as a subfield of linguistic semantics, is the study of word meanings. [ 1 ] [ 2 ] It includes the study of how words structure their meaning, how they act in grammar and compositionality , [ 1 ] and the relationships between the distinct senses and uses of a word.
Semantic properties or meaning properties are those aspects of a linguistic unit, such as a morpheme, word, or sentence, that contribute to the meaning of that unit.Basic semantic properties include being meaningful or meaningless – for example, whether a given word is part of a language's lexicon with a generally understood meaning; polysemy, having multiple, typically related, meanings ...
In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectoral representation of text (individual words or entire documents) that uses a document corpus as a knowledge base.
Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or ...