Matches in DBpedia 2014 for { <http://dbpedia.org/resource/Explicit_semantic_analysis> ?p ?o. }
Showing items 1 to 14 of
14
with 100 items per page.
- Explicit_semantic_analysis abstract "In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectorial representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the tf–idf matrix of the text corpus and a document (string of words) is represented as the centroid of the vectors representing its words. Typically, the text corpus is Wikipedia, though other corpora including the Open Directory Project have been used.ESA was designed by Evgeniy Gabrilovich and Shaul Markovitch as a means of improving text categorizationand 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 and described by humans", where Wikipedia articles (or ODP entries, or otherwise titles of documents in the knowledge base corpus) are equated with concepts.The name "explicit semantic analysis" contrasts with latent semantic analysis (LSA), because the use of a knowledge base makes it possible to assign human-readable labels to the concepts that make up the vector space.ESA, as originally posited by Gabrilovich and Markovitch, operates under the assumption that the knowledge base contains topically orthogonal concepts. However, it was later shown by Anderka and Stein that ESA also improves the performance of information retrieval systems when it is based not on Wikipedia, but on the Reuters corpus of newswire articles, which does not satisfy the orthogonality property; in their experiments, Anderka and Stein used newswire stories as "concepts".To explain this observation, links have been shown between ESA and the generalized vector space model.Gabrilovich and Markovitch replied to Anderka and Stein by pointing out that their experimental result was achieved using "a single application of ESA (text similarity)" and "just a single, extremely small and homogenous test collection of 50 news documents".Cross-language explicit semantic analysis (CL-ESA) is a multilingual generalization of ESA.CL-ESA exploits a document-aligned multilingual reference collection (e.g., again, Wikipedia) to represent a document as a language-independent concept vector. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations.".
- Explicit_semantic_analysis wikiPageExternalLink esa.html.
- Explicit_semantic_analysis wikiPageID "36472495".
- Explicit_semantic_analysis wikiPageRevisionID "575967794".
- Explicit_semantic_analysis hasPhotoCollection Explicit_semantic_analysis.
- Explicit_semantic_analysis subject Category:Natural_language_processing.
- Explicit_semantic_analysis subject Category:Vector_space_model.
- Explicit_semantic_analysis comment "In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectorial representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the tf–idf matrix of the text corpus and a document (string of words) is represented as the centroid of the vectors representing its words.".
- Explicit_semantic_analysis label "Explicit semantic analysis".
- Explicit_semantic_analysis sameAs m.0k8l4zb.
- Explicit_semantic_analysis sameAs Q5421270.
- Explicit_semantic_analysis sameAs Q5421270.
- Explicit_semantic_analysis wasDerivedFrom Explicit_semantic_analysis?oldid=575967794.
- Explicit_semantic_analysis isPrimaryTopicOf Explicit_semantic_analysis.