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

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

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

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.

this paper. 3,4 Compared to these others, our study includes new retrieval and feedback algorithms and a larger document collection

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

• 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

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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

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