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Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. [ 1] Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual ...
In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. A metalanguage based on predicate logic can analyze the speech of humans. [ 1]: 93- Another strategy to understand the ...
Distributional semantics [1] is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data. The basic idea of distributional semantics can be summed up in the so-called distributional ...
Latent semantic analysis ( LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces ...
t. e. In natural language processing(NLP), a word embeddingis a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valuedvector 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] Word embeddings can ...
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph -structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
In representation learning, knowledge graph embedding ( KGE ), also referred to as knowledge representation learning ( KRL ), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph 's entities and relations while preserving their semantic meaning.
e. Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous ...