scholarly journals Latent semantic indexing (LSI) and its impact on copywriting

2019 ◽  
pp. 4-12
Author(s):  
N. Blynova
Keyword(s):  
Search Engines ◽  
Ordinary Language ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
New Paradigm ◽  
Keywords And Phrases ◽  
The Media ◽  
Internet Environment ◽  
The Difference ◽  
Key Phrases

Latent semantic indexing (LSI) is becoming more and more popular in copywriting, gradually replacing texts written on the principles of SEO. LSI was called in the 2010s, when popular search engines switched to a qualitatively new way of ranking materials and sites. The difference between SEO and LSI ways of creation lies in the fact that search engines rank SEO materials by keywords, while LSI are ranked how fully the topic is covered and how useful the article will be to the reader. Consequently, in addition to keywords and phrases, the associative core is involved here. Materials written for people have replaced the texts created for the search engine. The article describes the algorithm for creation of the associative and thematic core, the ways in which this can be done. The basic steps helping to create an LSI text are also shown.The author underlines that due to the specificity of the presentation of a significant amount of information and the maximum expertise in the disclosure of the topic, text writers accustomed to working on the principles of SEO have to learn to write within a new paradigm. The owners of the websites that host articles created by LSI principles have discovered the advantages of this way of presenting information, since their resources have become better indexed and take the leading positions in search results. Such algorithms as “Baden-Baden”, “Korolev” and “Panda” have positively influenced the Internet environment as a whole, since re-optimized texts, which were filled with keys and were of little use to the reader, now have turned out to be on the last positions of issue. The new method of ranking according to the LSI method allows specialists to create the texts that are not only useful and expert but also differ in lexical richness, using expressive and figurative means of the language, which could not be assumed in SEO materials.It is highlighted in the article the use of neural networks should bring the way of presenting information to the consumer’s needs even more, inventing techniques that will allow leading materials created in an ordinary language to lead the positions without the need to incorporate key phrases into the text. We believe that the LSI-method, which has perfectly manifested itself in copywriting, is capable of unlocking the potential of the media texts, which are now being written on the principles of SEO.

scholarly journals Enhanced Latent Semantic Indexing Using Cosine Similarity Measures for Medical Application

2020 ◽  
Vol 17 (5) ◽  
pp. 742-749
Author(s):  
Fawaz Al-Anzi ◽  
Dia AbuZeina
Keyword(s):  
Language Processing ◽  
Search Engines ◽  
Dimensional Space ◽  
Similarity Measures ◽  
Medical Application ◽  
Latent Semantic Indexing ◽  
Arabic Language ◽  
Cosine Similarity ◽  
Semantic Indexing ◽  
Cosine Similarity Measures

The Vector Space Model (VSM) is widely used in data mining and Information Retrieval (IR) systems as a common document representation model. However, there are some challenges to this technique such as high dimensional space and semantic looseness of the representation. Consequently, the Latent Semantic Indexing (LSI) was suggested to reduce the feature dimensions and to generate semantic rich features that can represent conceptual term-document associations. In fact, LSI has been effectively employed in search engines and many other Natural Language Processing (NLP) applications. Researchers thereby promote endless effort seeking for better performance. In this paper, we propose an innovative method that can be used in search engines to find better matched contents of the retrieving documents. The proposed method introduces a new extension for the LSI technique based on the cosine similarity measures. The performance evaluation was carried out using an Arabic language data collection that contains 800 medical related documents, with more than 47,222 unique words. The proposed method was assessed using a small testing set that contains five medical keywords. The results show that the performance of the proposed method is superior when compared to the standard LSI

An Improved Semidiscrete Matrix Decomposition and its Application in Chinese Information Retrieval

2012 ◽  
Vol 241-244 ◽  
pp. 3121-3124 ◽  
Author(s):  
Yang Luo
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Matrix Decomposition ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
Storage Space ◽  
Important Direction ◽  
The Difference

Information retrieval is an important direction in the area of natural language processing .This paper introduced semidiscrete matrix decomposition in latent semantic indexing. We aimed at it’s disadvantage in storage space and presented SSDD,then we compare the difference of SVD and SDD and SSDD in performance

scholarly journals Perencanaan Search Engine E-commerce dengan Metode Latent Semantic Indexing Berbasis Multiplatform

2017 ◽  
pp. 31
Author(s):  
Ni Made Ari Lestari ◽  
Made Sudarma
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Word Processing ◽  
Latent Semantic Indexing ◽  
Electronic Data Interchange ◽  
Levenshtein Distance ◽  
Electronic Systems ◽  
Semantic Indexing ◽  
Automated Data Collection ◽  
Data Interchange

E-commerce is a sale and purchase transactions that occur through electronic systems such as the Internet, WWW, or other computer networks. E-commerce involves electronic data interchange and automated data collection systems. In all e-commerce search engine provided a column for the search items desired by the user. In e-commerce such as Tokopedia, Lazada, MatahariMall, Amazon, and other search engines that provided just use a regular search engine technology. In the usual search engines getting longer sentences from the input or output of goods search results will be more extensive and more. However, by utilizing the semantic indexing technology, the longer and clear input desired goods, the number of searches will be few and accurately in accordance with the input that helps the user in decision making. In this study discussed how to build a search engine on the web e-commerce by using Latent Semantic Indexing. The first starts from the use of Text Mining methods for word processing, and the method Levenshtein Distance to repair automatic word and the last Latent Semantic Indexing for information processing and input expenditure.

Optimasi Blog Dengan Meta Tag Untuk Seo Dan Monitoringnya

2017 ◽  
Vol 15 (1) ◽  
pp. 45
Author(s):  
Valian Yoga Pudya Ardhana ◽  
Ahmad Wilda Yulianto
Keyword(s):  
Search Engines ◽  
The Elderly ◽  
Monitoring Process ◽  
Main Target ◽  
The Media

Blog as one of the media applicationson the Internethas been used all aroundIndonesia. The user wasnot limited  by age,ranging from children to the elderly. A lot of people notrealize that blogs can beoptimizedso thatthe bloggettingtoppositionsin search engines. Metatagwasone ofoptimization techniquesinSearch Engine Optimization (SEO).The main target washow to increaseblogtraffi requests. Afteroptimization, the next stepwasmonitoring, whichaims to determinethe extent to whichthe success ofoptimizationhas been done onSEO.The resultwas ablog sitegettingtoppositionsinthe search enginesandthe monitoring process resultsindicatethat thetitleand content was veryappropriatethat was 100%, description and contentwere alsoappropriatethat was 91%.

scholarly journals Impact of media components from different suppliers on enterokinase productivity in Pichia pastoris

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ján Krahulec ◽  
Martin Šafránek
Keyword(s):  
Recombinant Proteins ◽  
Fermentation Process ◽  
High Impact ◽  
Production Level ◽  
Media Component ◽  
Component Supplier ◽  
The Media ◽  
The Difference ◽  
Optimisation Procedure ◽  
The Ideal

Abstract Background The aim of this study was to provide an information about the homogeneity on the level of enterokinase productivity in P. pastoris depending on different suppliers of the media components. Results In previous studies, we performed the optimisation process for the production of enterokinase by improving the fermentation process. Enterokinase is the ideal enzyme for removing fusion partners from target recombinant proteins. In this study, we focused our optimization efforts on the sources of cultivation media components. YPD media components were chosen as variables for these experiments. Several suppliers for particular components were combined and the optimisation procedure was performed in 24-well plates. Peptone had the highest impact on enterokinase production, where the difference between the best and worst results was threefold. The least effect on the production level was recorded for yeast extract with a 1.5 fold difference. The worst combination of media components had a activity of only 0.15 U/ml and the best combination had the activity of 0.88 U/ml, i.e., a 5.87 fold difference. A substantially higher impact on the production level of enterokinase was observed during fermentation in two selected media combinations, where the difference was almost 21-fold. Conclusions Results demonstrated in the present study show that the media components from different suppliers have high impact on enterokinase productivity and also provide the hypothesis that the optimization process should be multidimensional and for achieving best results it is important to perform massive process also in terms of the particular media component supplier .

The Question of the Future of Indian Muslims in Malaysia: The Post-Mahathir Legacy Era

2012 ◽  
Vol 11 (1-2) ◽  
pp. 219-246 ◽  
Author(s):  
Ahmad Noor Sulastry Yurni
Keyword(s):  
Paradigm Shift ◽  
Social Economic ◽  
Ethnic Politics ◽  
Muslim Community ◽  
British Colonialism ◽  
Way Of Life ◽  
New Paradigm ◽  
Economic Allocation ◽  
Islamic Resurgence ◽  
The Difference

Abstract Abstract The Malays, Chinese and Indian community in Malaysia have been homogenized since British colonialism. The existence of Indian Muslims’ identity caused a new paradigm shift in Malaysia involving the racial discussion. This paper traces the difference in Indian Muslims’ identities from Indian and the Hindus. I argued that Indian Muslims share Islam as their religion and faith, while maintaining a Malay way of life and custom in their daily practices. In Malaysia, the Indian Muslim community struggled to place their future in terms of social, economic allocation and political justification among the other communities. However, the strength of ethnic politics clearly charted out their involvement in the political base and moved them to fight for their cause and rights. Hence, today’s Indian Muslim community has caused an Islamic resurgence, which has brought a new Indian dimension as a whole.

Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing

2008 ◽  
Vol 7 (1) ◽  
pp. 182-191 ◽  
Author(s):  
Sebastian Klie ◽  
Lennart Martens ◽  
Juan Antonio Vizcaíno ◽  
Richard Côté ◽  
Phil Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Semantic Indexing ◽  
Semantic Indexing

A Method Based on Support Vector Machine for Feature Selection of Latent Semantic Features

2011 ◽  
Vol 181-182 ◽  
pp. 830-835
Author(s):  
Min Song Li
Keyword(s):  
Support Vector Machine ◽  
Text Categorization ◽  
Latent Semantic Indexing ◽  
Classification Performance ◽  
Compact Representation ◽  
Support Vector ◽  
Semantic Features ◽  
Semantic Indexing ◽  
Feature Extraction Method ◽  
Feature Subspace

Latent Semantic Indexing(LSI) is an effective feature extraction method which can capture the underlying latent semantic structure between words in documents. However, it is probably not the most appropriate for text categorization to use the method to select feature subspace, since the method orders extracted features according to their variance,not the classification power. We proposed a method based on support vector machine to extract features and select a Latent Semantic Indexing that be suited for classification. Experimental results indicate that the method improves classification performance with more compact representation.

Sign in / Sign up

Export Citation Format

Share Document