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| | MIR-essay.txt |
 | | In the Vector-Space model, documents and queries are represented as vectors in a vector space spanned by the index terms, and uncertainty is modeled by considering geometric similarity. | | | The degree of similarity of the document dj with regard to the query q is calculated as the cosine of the angle A. The vector space model ranks the documents according to their degree of similarity to the query. | | | Language models compute the probability that a given document model could have produced the current query and ranks the documents in descending order of probability. |
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http://www.student.dcu.ie/~brownel2/MIR-essay.txt
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| | Proposal |
| | One main advantage of vector space models is that they can rank the relevant documents according to their similarity to the query precisely and effectively. | | | In the retrieval process, the system compares the position of the query to the location of each document in the vector space and ranks the documents according to their degree of similarity to the query. | | | Another drawback of vector space models is that for large text collection the term-by-document matrix is always a huge and sparse one, which needs a lot of disk storage space. |
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http://www.cs.ualberta.ca/~stroulia/EduNuggets/InformationRetrieval
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| | Paper: :: |
| | The purpose of using the vector space model is to nd numerical and geometrical relationships between document vectors and a query vector in order to nd the documents that are most relevant to the query. | | | Then the purpose of this paper is to outline the vector space model, to explain two methods of making the vector space model a more e cient system of information retrieval, and to introduce an example using the system. | | | The cosine of the angles between the document vectors in the example database and the example query vector are 0, 0, 0.4083, 0.7071, and 0.6324. |
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http://computing.breinestorm.net/document+vector+term+model+space
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| | System Model |
| | We used the vector space model (VSM) [ FO95 ] in our system which is established as an effective and the de-facto standard model for web IR engines to represent documents. | | | There are different inverted index organizations which differ in their time and space complexities, and one of the primary aims of this project is to evaluate the relative performance of two most widely used document indexing methods in a realistic experimental setting. | | | This is accomplished by computing the length of the vector representing the document and dividing the weights of the terms by this value, i.e., the length of vector |
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http://www.cs.umd.edu/~rich/courses/cmsc710-f97/projects/indexing/node2.html
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| | CS20 Final Project: Matrices, Vector Spaces, and Information Retrieval |
| | In the Vector Space Model, queries are made to the database by using a query vector, a vector very much like a document vector, as they are the same dimensions. | | | However, the sparcity of the vectors, especially the query vector, is a key feature in the model. | | | However, using cosines, we can take advantage of the sparsity of the query vector, and only compute those multiplications (to get the numerator in the equation) in which the query entry is non-zero. |
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http://www.ugcs.caltech.edu/~chandran/cs20/whatisvsm.html
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| | annsa_pp.doc |
| | Matching was done with the vector space model, and the evaluation of the program implies that it works reasonably well. | | | There are three basic types of IR systems: Boolean (exact matching) Probabilistic (inexact matching) Vector Space (inexact matching) The vector space model, or vector model, views documents and queries as term vectors in an M-dimensional space, where M is the total number of indexed terms in all documents. | | | Vectors are compared with different similarity measures, where the most commonly known is the cosine coefficient. |
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http://www.ida.liu.se/~TDDB55/HT99/pp_reports/annsa_pp.doc
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| | Citations: A vector-space model for information retrieval - Salton, Yang, Wong (ResearchIndex) |
| | When the vectors are normalized to unit length, then the inner product is equal to measuring the cosine of the angle between the two vectors in the vector space. | | | In the vector space model, every information item including the stored texts and any natural language information request is stored as a set, or vector, of terms. | | | The algorithm is based upon the fact that if the relevance for a query is known, an optimal 1 query vector will maximize the average query document similarity for the relevant articles, and will simultaneously minimize the average query document similarity for the non relevant documents. |
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http://citeseer.lcs.mit.edu/context/294335/0
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| | A Unified Algebraic Framework for Classical Geometry |
| | There the models are constructed in Minkowski space by projective splits with respect to a fixed vector of null or negative signature. | | | This deficiency in the vector space model was corrected early in the 19th century by removing the origin from the plane and placing it one dimension higher. | | | This representation was not available to Grassmann, because he did not have the concept of null vector. |
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http://modelingnts.la.asu.edu/html/UAFCG.html
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| | Library Trends: The most influential paper Gerard Salton never wrote |
| | Evidently, the familiarity of vector space illustrations has led to a confounding of objective facts (that term dependencies and word associations exist) with implications for how those facts might be modeled (as correlations between vectors in a vector space). | | | But that position is equally problematic: if the basis vectors represent index terms then those vectors are not assumed to be orthogonal, they simply are orthogonal, because all that the vectors represent is the way that term frequency data are used in the system's computations. | | | When a commentator on the VSM says that term basis vectors are assumed to be orthogonal, this is a misstating of the actual fact that dependencies among words in natural language are ignored. |
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http://www.findarticles.com/p/articles/mi_m1387/is_4_52/ai_n7074022/pg_4
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| | Elements of Hypermedia Design - 1.4 The Vector Space Model |
| | Contrary to the basic Boolean query model, the vector space model allows to find the documents which are the most similar to the query without the need for a 100 percent match. | | | In the vector space model, both queries and documents are represented as term vectors of the form Di = (di1, di2,...,dit) and Q = (q1, q2,...,qt). | | | A document collection is then represented as a term-document matrix A: The similarity between a query vector Q and a document term vector D can then be computed as: |
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http://www.ickn.org/elements/hyper/cyb8.htm
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| | similarities.html |
| | In the Vector Space Model queries are resolved by first translating the query to a vector with the same arity n as the documents, comparing them as shown here and then ranking the similarities. | | | In table 5.1 (constructed) examples are given of three such vector spaces: apart from the document space, we give also the examples of a nationality space and a keyword space. | | | In the middle part of the table, document space may be demonstrated intuitively by comparing the fact that documents with similar contents tend to have the same, or similar words. |
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http://pi0959.kub.nl/Paai/Onderw/V-I/Content/similarities.html
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| | Vector Space Tutorial |
| | Vector Space Search Engine Building a Spider Indexing the Internet Vector Space Postgres Programming What is a robots.txt file Stop List | | | What we have been doing is moving around in a 2 dimensional space using Vectors as a sort of map. | | | This vector is a pointer to a place in an N dimensional space where N = 11. |
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http://www.thebananatree.org/vector_space/vector_space.html
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| | vector space model..... |
| | Might be a good idea to ask google: what's the vector space model of query... | | | Then, a vector similarity function, such as the inner product, is used to compute the relevance between the query and a document in Goo index. | | | m -th component of the vector is associated with the term frequency in the query. |
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http://www.webmasterworld.com/forum34/511.htm
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| | perl.com: Building a Vector Space Search Engine in Perl |
| | To search our collection, we project a query into this term space and calculate the distance from the query vector to all the document vectors in turn. | | | Like any vector, it has a magnitude (determined by how many times each word occurs), and a direction (determined by which words appeared, and their relative abundance). | | | Because a document's position in the term space is determined by the words it contains, documents with many words in common end up close together, while documents with few shared words end up far apart. |
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http://www.perl.com/pub/a/2003/02/19/engine.html
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| | Term Vector Theory and Keyword Weights |
| | All variants of Salton's Term Vector Model -a keystone in information retrieval studies- demonstrate that the weight of a term in a document is determined with a combination of global (database level) and local (document level) measures. | | | This thread is about term vector models and how term weights are computed by search engines and IR systems. | | | This is a term count model, historically one of the first variants of the vector model. |
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http://forums.searchenginewatch.com/showthread.php?t=489
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| | Department of Energy Information Bridge - full-text scientific and technical reports (gray literature) |
| | One possible approach to this problem i use the vector space model, which models documents and queries as vectors in the term space. | | | The components of the vectors are determined by the term weighting scheme, a function of the frequencies of the terms in the document or query as well as throughout the collection. | | | The goal in information retrieval is to enable users to automatically and accurately find data relevant to their queries. |
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http://www.osti.gov/bridge/product.biblio.jsp?osti_id=5698
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| | A unified approach to high-performance, vector-based information retrieval |
| | An information retrieval model based on the vector space model is proposed that unifies and extends many commonly used retrieval mechanisms. | | | The model and algorithm are designed for retrieval from a corpus of information objects in a single subject area. | | | Finally, the model allows for content labels that are semantically more complex than just attributes, keywords and subject classifications. |
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http://www.ccs.neu.edu/home/kenb/key/unified/unified.html
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| | The Term Vector Database: fast access to indexing terms for Web pages |
| | Each document's vector seeks to represent the document in a "vector space", allowing comparison with vectors derived from other sources, for example, queries or other documents. | | | In the vector space model of information retrieval ([Salton 71]), documents are modeled as vectors in a high-dimensional space of many thousands of terms. | | | A topic vector is a term vector computed from the text of pages in the seed set. |
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http://www9.org/w9cdrom/159/159.html
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| | myreview.txt |
| | Conceptualization of geo-space as cube - as opposed to vector space. | | | Summarize in 2-3 sentences the main contribution of this paper: The author proposed a geographic information retrieval model (YYY) that uses two ways for indexing geographical documents that are self-complementary: geographical model and the traditional (textual) vector space model. | | | Most of the paper is taken by background material, and thus there is very little space left to the actual model and its implementation. |
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http://www.dcc.unicamp.br/~cmbm/MO812/myreview.txt
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| | SIGIR: SIGIR '86, On extending the vector ... |
| | An information retrieval model, named the Generalized Vector Space Model (GVSM), is extended to handle situations where queries are specified as (extended) Boolean expressions. | | | Although the experimental results for extended Boolean retrieval are not always better than the vector processing method, the developments here are significant in facilitating commercially available retrieval systems to benefit from the vector based methods. | | | The query language extension is attractive in the sense that most of the algebraic properties of the strict Boolean language are still preserved. |
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http://widit.slis.indiana.edu/irpub/SIGIR/1986/cite21.htm
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| | IR Technologies in modern search engines: The Vector Space Model |
| | Search queries are projected as vectors (implying magnitude and direction) while documents are mapped as vector points in term space according to their conceptual relationship to appropriate terms. | | | The Vector Space Model (Salton's Vector Space Model or Term Vector Model) is a multidimensional model of terms arranged according to a semantic-proximity scheme that situates lexicon terms in space according to their conceptual ontology. | | | Queries travel along a vector which represents relation to terms while Query and Document weights are based on the length and direction of their vector. |
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http://www.greenbuilt-research.com/search-technology/archive/ir-vector.html
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| | Manifesto for the Reputation Society |
| | In the vector space model, documents are still represented as lists of words, but the number of times each word occurs is also stored — so the index would have an entry similar to "In this document, ‘a’ occurs 127 times, ‘aardvark’ occurs 1 time,...". | | | Space for insightful yet unpopular points of view may ironically be provided by the same polarizing phenomenon that discounts minority views, since easy polarization implies that differing points of view can easily be filtered out and ignored. | | | The system then calculates the closeness between the query and each document, where they are considered closer when they both have high values for the same entries. |
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http://www.firstmonday.dk/issues/issue9_7/masum
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| | Chapter 3 Vector Space Methods |
| | Using several simplifications of the vector-space model for text retrieval queries, the authors seek the optimal balance between processing efficiency and retrieval effectiveness as expressed in relevant documnet rankings. | | | If we represent the collection as a term-by-document matrix, a query is another document vector | | | Search: Document vectors "close" to the query vector |
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http://www.eng.mu.edu/corlissg/168Search.03F/ch3.html
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| | Machine Learning Applied to Information Retrieval |
| | After demonstrating that our system is able to learn a model of a user with a single well-defined interest, we present an initial experiment where over the course of 24 days the output of our system was compared to both randomly-selected and human-selected pages. | | | Proposes a model, inspired by recent artificial life theory, applied to the problem of retrieving information from a large, distributed collection of documents such as the World Wide Web. | | | They do this by examining the user's ordering on test queries, and altering the parameters of the model by a gradient-based optimization method. |
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http://www.csi.uottawa.ca/~debruijn/irbib.html
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| | Hypertext'03 Conference: Complete List of Papers |
| | The proposed model is being tested in a gaming and storytelling environment that integrates the real world, media elements and virtual 3D worlds. | | | The game is realized as a hypermedia document in which geo-referenced hyperlinks on a map lead to the hypermedia documents that form the riddles that are to be solved at the different physical checkpoints. | | | This leads us to draw a number of conclusions about the benefits and disadvantages of both and the concessions that are required to combine them successfully. |
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http://www.ht03.org/papers
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| | Metaphor and the Space Structuring Model |
| | For this reason it is crucial for establishing the overall meaning, which involves comprehension of the relationship between the cognitive models in the source input, the target input, and the blended space. | | | Conceptual structure in the two input spaces, then, are analogically linked, while the mappings between the inputs and the generic space involve category inclusion. | | | Blends have two or more input spaces structured by information from discrete cognitive domains, a generic space that contains abstract structure common to all spaces in the network, and a blended space that contains selected aspects of structure from both input spaces, as well as emergent structure of its own. |
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http://cogsci.ucsd.edu/~coulson/ssm.htm
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| | Model-Based Simulation for Civil and Mechanical Systems |
| | Model updating involves reducing the uncertainty of the simulations either by reduced uncertainty in the data or improving the model itself. | | | Models may be distinguished by their level of abstraction of the physics in the system. | | | The modeling of uncertainty and propagation of uncertainty from model parameters to a probabilistic estimate of performance involves research challenges when dealing with complex systems of different materials and components. |
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http://caswww.colorado.edu/MBS.Workshop.d/WhitePaper.d/MBS.WhitePaper.final.html
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