Vector Space Model Of Information Retrieval Research Articles

Visualization for Information Retrieval in Regional Distributed Environment

Information retrieval (IR) is the task of representing, storing, organizing, and offering access to information items. The problem for search engines is not only to find topic relevant results, but results consistent with the user’s information need. How to retrieve desired information from the Internet with high efficiency and good effectiveness is become the main concern of internet user-based. The interface of the systems does not help them to perceive the precision of these results. Speed, resources consuming, searching and retrieving process also aren't optimal. The search engine's aim is developing and improving the performance of information retrieval system and gifting the user whatever his culture' level. The proposed system is using information visualization for interface problems, and for improving other side of web IR system's problems, it uses the regional crawler on distributed search environment with conceptual query processing and enhanced vector space information retrieval model (VSM). It is an effective attempt to match renewal user's needs and get a better performance than ordinary system.

A relational vector space model using an advanced weighting scheme for image retrieval

In this paper, we lay out a relational approach for indexing and retrieving photographs from a collection. The increase of digital image acquisition devices, combined with the growth of the World Wide Web, requires the development of information retrieval (IR) models and systems that provide fast access to images searched by users in databases. The aim of our work is to develop an IR model suited to images, integrating rich semantics for representing this visual data and user queries, which can also be applied to large corpora. Our proposal merges the vector space model of IR – widely tested in textual IR – with the conceptual graph (CG) formalism, based on the use of star graphs (i.e. elementary CGs made up of a single relation connected to some concepts representing image objects). A novel weighting scheme for star graphs, based on image objects size, position, and image heterogeneity is outlined. We show that integrating relations into the vector space model through star graphs increases the system’s precision, and that the results are comparable to those from graph projection systems, and also that they shorten processing time for user queries.

A relational vector-space model of information retrieval adapted to images

The increase of digital image acquisition devices, combined to the growth of the Web, requires the definition of Information Retrieval (IR) models and systems providing fast access to images searched by users among large amounts of data.

Simple PCAを用いたベクトル空間情報検索モデルの次元削減

In this paper, we propose to use the Simple Principal Component Analysis (SPCA) for dimensionality reduction of the vector space information retrieval model. The SPCA algorithm is a data-oriented fast method which does not require the computation of the variance-covariance matrix. In SPCA, principal components are estimated iteratively so we also propose a criteria to determine the convergence. The optimum number of iterations for each principal component can be determined using the criteria. Experimentally, we show that the SPCA-based method offers improvement over the conventional SVD-based method despite its small amount of computation. This advantage of SPCA can be attributed to its iterative procedure which is similar to clustering methods such as k-means clustering. On the other hand, the proposed method which orthogonalizes the basis vectors also achieved much higher accuracy than the conventional random projection method based on k-means clustering.

A month to topic detection and tracking in Hindi

We describe the one-month (June 2003) effort to create a topic detection and tracking (TDT) system to support news stories in Hindi. The University of Massachusetts submitted results for three different TDT tasks in the DARPA surprise language evaluation. The official task was topic tracking, but we also provided results for the new event detection and topic detection (clustering) tasks. Our approach to all three tasks was based on the vector-space model of information retrieval. We also describe the process we used to create the relevance judgments used to evaluate the system. Results suggest that topic tracking effectiveness is comparable to that of TDT tracking systems in other languages. Results for clustering and new event detection indicate that parameter settings for those tasks are sensitive to the language being used.

Recovering traceability links between code and documentation

Software system documentation is almost always expressed informally in natural language and free text. Examples include requirement specifications, design documents, manual pages, system development journals, error logs, and related maintenance reports. We propose a method based on information retrieval to recover traceability links between source code and free text documents. A premise of our work is that programmers use meaningful names for program items, such as functions, variables, types, classes, and methods. We believe that the application-domain knowledge that programmers process when writing the code is often captured by the mnemonics for identifiers; therefore, the analysis of these mnemonics can help to associate high-level concepts with program concepts and vice-versa. We apply both a probabilistic and a vector space information retrieval model in two case studies to trace C++ source code onto manual pages and Java code to functional requirements. We compare the results of applying the two models, discuss the benefits and limitations, and describe directions for improvements.

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.

教師あり学習によるベクトル空間情報検索モデルの精度改善

教師あり学習によるベクトル空間情報検索モデルの精度改善