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Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content [citation needed] as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of ...
The lexical route is the process whereby skilled readers can recognize known words by sight alone, through a "dictionary" lookup procedure. [1] [4] According to this model, every word a reader has learned is represented in a mental database of words and their pronunciations that resembles a dictionary, or internal lexicon.
Distributional semantic models have been applied successfully to the following tasks: finding semantic similarity between words and multi-word expressions; word clustering based on semantic similarity; automatic creation of thesauri and bilingual dictionaries; word sense disambiguation; expanding search requests using synonyms and associations;
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.
Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word. An alternative to the use of the definitions is to consider general word-sense relatedness and to compute the semantic similarity of each pair of word senses based on a given lexical knowledge base such as ...
Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results.
Similarity search is the most general term used for a range of mechanisms which share the principle of searching (typically very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects ...
ESA was designed by Evgeniy Gabrilovich and Shaul Markovitch as a means of improving text categorization [2] and has been used by this pair of researchers to compute what they refer to as "semantic relatedness" by means of cosine similarity between the aforementioned vectors, collectively interpreted as a space of "concepts explicitly defined ...