The Optimization Algorithm Based on Vector Space Model to Determine the Influence of Microblogging Text

2014 ◽  
Vol 571-572 ◽  
pp. 278-281 ◽  
Author(s):  
Yu Chen Dong ◽  
Yan Liu ◽  
Jun Yong Luo ◽  
Jin Zhang
Keyword(s):  
Impact Factor ◽  
Vector Space ◽  
Optimization Algorithm ◽  
Real World ◽  
Information Dissemination ◽  
Evaluation Method ◽  
Vector Space Model ◽  
Clustering Method ◽  
Space Model ◽  
Dissemination Process

This article uses the vector space model to express microblogging text in quantity, via the cosine of the angle of text clustering method to calculate the similarity between the text. We can generate alternative microblogging hot cluster by centralized method, by adding the "microblogging impact Factor " into vector text mold of alternative hotspot clusters. In order to simulate the exclusive advantage of "star" in the information dissemination process of the real world, we use the alternative hotspot clusters which already weighted the "microblogging impact factor" to re-evaluate its characteristics of vector, to optimize the hot microblogging discovery algorithm and to make the sorting results more in line with the law of information dissemination, finally to give some suggestions and opinions on hot microblogging evaluation method.

scholarly journals Looking for Semantic Similarity: What a Vector Space Model of Semantics Can Tell Us About Attention in Real-world Scenes

2021 ◽  
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Real World ◽  
Critical Role ◽  
Vector Space Model ◽  
Semantic Knowledge ◽  
Space Model ◽  
Strong Positive Relationship ◽  
Space Semantic ◽  
The Relationship

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real- world scenes but is poorly understood. Here we examined the relationship between object semantics and attention by combining a vector space model of semantics with eye movements in scenes. Within this approach, the vector space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention, with greater attention to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

scholarly journals Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

2021 ◽  
pp. 095679762199476
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Real World ◽  
Critical Role ◽  
Vector Space Model ◽  
Semantic Knowledge ◽  
Space Model ◽  
Strong Positive Relationship ◽  
Space Semantic ◽  
The Relationship

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

Aplikasi Deteksi Kemiripan Tugas Paper

2017 ◽  
Vol 15 (2) ◽  
pp. 5
Author(s):  
Anthony Anggrawan ◽  
Azhari
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Mean Average Precision ◽  
Average Precision ◽  
Information Searching ◽  
Space Model ◽  
Model Method

Information searching based on users’ query, which is hopefully able to find the documents based on users’ need, is known as Information Retrieval. This research uses Vector Space Model method in determining the similarity percentage of each student’s assignment. This research uses PHP programming and MySQL database. The finding is represented by ranking the similarity of document with query, with mean average precision value of 0,874. It shows how accurate the application with the examination done by the experts, which is gained from the evaluation with 5 queries that is compared to 25 samples of documents. If the number of counted assignments has higher similarity, thus the process of similarity counting needs more time, it depends on the assignment’s number which is submitted.

Aplikasi Rekomendasi Buku Pada Katalog Perpustakaan Universitas Multimedia Nusantara Menggunakan Vector Space Model

2018 ◽  
Vol 9 (2) ◽  
pp. 97-105
Author(s):  
Richard Firdaus Oeyliawan ◽  
Dennis Gunawan
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Vector Model ◽  
Library Management ◽  
Space Model ◽  
Library Management System ◽  
Index Terms ◽  
Library Catalogue ◽  
Language Sample ◽  
F Measure

Library is one of the facilities which provides information, knowledge resource, and acts as an academic helper for readers to get the information. The huge number of books which library has, usually make readers find the books with difficulty. Universitas Multimedia Nusantara uses the Senayan Library Management System (SLiMS) as the library catalogue. SLiMS has many features which help readers, but there is still no recommendation feature to help the readers finding the books which are relevant to the specific book that readers choose. The application has been developed using Vector Space Model to represent the document in vector model. The recommendation in this application is based on the similarity of the books description. Based on the testing phase using one-language sample of the relevant books, the F-Measure value gained is 55% using 0.1 as cosine similarity threshold. The books description and variety of languages affect the F-Measure value gained. Index Terms—Book Recommendation, Porter Stemmer, SLiMS Universitas Multimedia Nusantara, TF-IDF, Vector Space Model

On Generalized Vector Space Model in Information Retrieval

1985 ◽  
Vol 8 (2) ◽  
pp. 253-267
Author(s):  
S.K.M. Wong ◽  
Wojciech Ziarko
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
A Priori ◽  
Vector Space Model ◽  
Smart System ◽  
Space Model ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Index Terms ◽  
Minimal Modification

In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems.

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