Ranking of Odia Text Document Relevant to User Query Using Vector Space Model

2019 ◽  
Author(s):  
Upasana Parida ◽  
Mamata Nayak ◽  
Ajit Ku. Nayak
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Space Model ◽  
Text Document ◽  
User Query

Analysis of Vector Space Method in Information Retrieval for Smart Answering System

2020 ◽  
Vol 17 (9) ◽  
pp. 4468-4472
Author(s):  
Deepa Yogish ◽  
T. N. Manjunath ◽  
Ravindra S. Hegadi
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Query Term ◽  
Frequency Method ◽  
Document Ranking ◽  
User Intent ◽  
Space Model ◽  
Relevant Document ◽  
User Query

In the world of internet, searching play a vital role to retrieve the relevant answers for the user specific queries. The most promising application of natural language processing and information retrieval system is Question answering system which provides directly the accurate answer instead of set of documents. The main objective of information retrieval is to retrieve relevant document from a huge volume of data sets underlying in the internet using appropriatemodel. There are many models proposed for retrieval process such as Boolean, Vector space and Probabilistic method. Vector space model is best method in information retrieval for document ranking with efficient document representation which combines simplicity and clarity. VSM adopts similarity function to measure the matching between documents and user intent, and assign scores from the biggest to smallest. The documents and query are assigned with weights using term frequency and inverse document frequency method. To retrieve most relevant document to the user query term, document ranking function cosine similarity score is applied for every document and user query. The documents having more similarity scores will be considered as relevant documents to the query term and they are ranked based on these scores. This paper emphasizes on different techniques of information retrieval and Vector Space Model offers a realistic compromise in IR processing. It allows best weighing scheme which ranks the set of documents in order of relevance based on user query.

scholarly journals Best Approximate of Vector Space Model by Using SVD

2018 ◽  
Vol 28 (2) ◽  
pp. 143
Author(s):  
Raghad M. Hadi
Keyword(s):  
Text Mining ◽  
Vector Space ◽  
Document Clustering ◽  
Vector Space Model ◽  
Internet Technology ◽  
Low Rank ◽  
Space Model ◽  
Text Document ◽  
Space Technique ◽  
Text Mining Application

A quick growth of internet technology makes it easy to assemble a huge volume of data as text document; e. g., journals, blogs, network pages, articles, email letters. In text mining application, increasing text space of datasets represent excessive task which makes it hard to pre-processing documents in efficient way to prepare it for text mining application like document clustering. The proposed system focuses on pre-processing document and reduction document space technique to prepare it for clustering technique. The mutual method for text mining problematic is vector space model (VSM), each term represent a features. Thus the proposed system create vector-space mod-el by using pre-processing method to reduce of trivial data from dataset. While the hug dimen-sionality of VSM is resolved by using low-rank SVD. Experiment results show that the proposed system give better document representation results about 10% from previous approach to prepare it for document clustering

scholarly journals THE INFLUENCE OF TEXT PREPROCESSING METHODS AND TOOLS ON CALCULATING TEXT SIMILARITY

2019 ◽  
pp. 973
Author(s):  
Đorđe Petrović ◽  
Milena Stanković
Keyword(s):  
Text Mining ◽  
Vector Space ◽  
Vector Space Model ◽  
Text Similarity ◽  
Text Documents ◽  
Space Model ◽  
Text Document ◽  
The Subject ◽  
Text Preprocessing ◽  
Multidimensional Representation

Text mining to a great extent depends on the various text preprocessing techniques. The preprocessing methods and tools which are used to prepare texts for further mining can be divided into those which are and those which are not language-dependent. The subject matter of this research was the analysis of the influence of these methods and tools on further text mining. We first focused on the analysis of the influence on the reduction of the vector space model for the multidimensional represen-tation of text documents. We then analyzed the influence on calculating text similarity, which is the focus of this research. The conclusion we reached is that the implemen-tation of various text preprocessing methods in the Serbian language, which are used for the reduction of the vector space model for the multidimensional representation of text document, achieves the required results. But, the implementation of various text preprocessing methods specific to the Serbian language for the purpose of calculating text similarity can lead to great differences in the results.

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

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