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Topic-based Vector Space Model

by Jörg Becker, Dominik Kuropka - In Proceedings of the 6th International Conference on Business Information Systems , 2003
"... This paper motivates and presents the Topic-based Vector Space Model (TVSM), a new vector-based approach for document comparison. The approach does not assume independence between terms and it is flexible regarding the specification of term-similarities. Stop-word-list, stemming and thesaurus can be ..."
Abstract - Cited by 16 (0 self) - Add to MetaCart
This paper motivates and presents the Topic-based Vector Space Model (TVSM), a new vector-based approach for document comparison. The approach does not assume independence between terms and it is flexible regarding the specification of term-similarities. Stop-word-list, stemming and thesaurus can

Multi-Prototype Vector-Space Models of Word Meaning

by Joseph Reisinger, Raymond J. Mooney
"... Current vector-space models of lexical semantics create a single “prototype ” vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense-specific ..."
Abstract - Cited by 47 (2 self) - Add to MetaCart
Current vector-space models of lexical semantics create a single “prototype ” vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense

Using Vector-Space Model in Adaptive Hypermedia for Learning

by Jaakko Kurhila, Matti Lattu, Anu Pietilä
"... Abstract. Although many of the existing adaptive learning environments use other approaches, the vector-space model for information retrieval can be used in providing individualized learning with hypermedia. A system employing a modified version of the vector-space model is described. The approach t ..."
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Abstract. Although many of the existing adaptive learning environments use other approaches, the vector-space model for information retrieval can be used in providing individualized learning with hypermedia. A system employing a modified version of the vector-space model is described. The approach

Coupling Named Entity Recognition, Vector-Space Model and

by Knowledge Bases For, Sinequa S. A. S, Me Ledru Rollin
"... In this paper, we present a question-answering system combining Named Entity Recognition, VectorSpace Model and Knowledge Bases to validate answers candidates. Applying this hybrid approach, for our first participation in the TREC Q&A. ..."
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In this paper, we present a question-answering system combining Named Entity Recognition, VectorSpace Model and Knowledge Bases to validate answers candidates. Applying this hybrid approach, for our first participation in the TREC Q&A.

Document Ranking and the Vector-Space Model

by Ranking And The, Using Several
"... this paper. 3,4 Compared to these others, our study includes new retrieval and feedback algorithms and a larger document collection ..."
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this paper. 3,4 Compared to these others, our study includes new retrieval and feedback algorithms and a larger document collection

Vector Space Model and Clustering

by C. Faloutsos, C. Faloutsos
"... • primary key indexing • secondary key / multi-key indexing • spatial access methods • fractals • text • multimedia 15-826 Copyright: C. Faloutsos (2010) 4 ..."
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• primary key indexing • secondary key / multi-key indexing • spatial access methods • fractals • text • multimedia 15-826 Copyright: C. Faloutsos (2010) 4

• Vector Space Model

by Lingjia Deng
"... • e.g. term-document co-occurrence matrix A • limitation: high dimension • solution: principal component of the original matrix, to lower the dimension • PCA • subtract the mean • get the co-variance matrix • calculate the eigenvalues and eigenvectors of co-variance matrix • SVD • A = USVT • U: left ..."
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• e.g. term-document co-occurrence matrix A • limitation: high dimension • solution: principal component of the original matrix, to lower the dimension • PCA • subtract the mean • get the co-variance matrix • calculate the eigenvalues and eigenvectors of co-variance matrix • SVD • A = USVT • U: left eigenvectors • V: right eigenvectors • S: diagonal matrix of eigenvalues

Lexical Vector Space Models

by Pd Dr, Sabine Schulte Walde, Universität Zürich, Kolloquium In Computerlinguistik , 2015
"... st itu tf ür M as ch in el le S pr ac hv er ar be itu ..."
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st itu tf ür M as ch in el le S pr ac hv er ar be itu

Naming Functions for the Vector Space Model

by Yannis Tzitzikas, Yannis Theoharis
"... Abstract. The Vector Space Model (VSM) is probably the most widely used model for retrieving information from text collections (and recently from over other kinds of corpi). Assuming this model, we study the problem of finding the best query that ”names ” (or describes) a given (unordered or ordered ..."
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Abstract. The Vector Space Model (VSM) is probably the most widely used model for retrieving information from text collections (and recently from over other kinds of corpi). Assuming this model, we study the problem of finding the best query that ”names ” (or describes) a given (unordered

From frequency to meaning : Vector space models of semantics

by Peter D. Turney, Patrick Pantel - Journal of Artificial Intelligence Research , 2010
"... Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are begi ..."
Abstract - Cited by 347 (3 self) - Add to MetaCart
Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics
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