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Final school publication draft
Final school publication draft
Sanusia1
Young2
Young2
Glenys Young
Web Search Engines are best gifts to the mankind by Information and Communication Technologies. Without the search engines it would have been almost impossible to make the efficient access of the information available on the web today. They play a very vital role in the accessibility and usability of the internet based information systems. As the internet users are increasing day by day so is the amount of information being available on web increasing. But the access of information is not uniform across all the language communities. Besides English and European languages that constitutes to the 60% of the information available on the web, there is still a wide range of the information available on the internet in different languages too. In the past few years the amount of information available in Indian Languages has also increased. Besides English and few European Languages, there are no tools and techniques available for the efficient retrieval of this information available on the internet. Especially in the case of the Indian Languages the research is still in the preliminary steps. There are no sufficient amount of tools and techniques available for the efficient retrieval of the information for Indian Languages. As we know that Indian Languages are very resource poor languages in terms of IR test data collection. So my main focus was mainly on developing the data set for URDU IR, training and testing data for Stemmer. We have developed a language independent system to facilitate efficient retrieval of information available in Urdu language which can be used for other languages as well. The system gives precision of 0.63 and the recall of the system is 0.8. For this Firstly I have developed an Unsupervised Stemmer for URDU Language [1] as it is very important in the Information Retrieval.
An unsupervised approach to develop ir system the case of urdu
An unsupervised approach to develop ir system the case of urdu
ijaia
In cross-lingual plagiarism detection, the similarity between sentences is the basis of judgment. This paper proposes a discriminative model trained on bilingual corpus to divide a set of sentences in target language into two classes according their similarities to a given sentence in source language. Positive outputs of the discriminative model are then ranked according to the similarity probabilities. The translation candidates of the given sentence are finally selected from the top-n positive results. One of the problems in model building is the extremely imbalanced training data, in which positive samples are the translations of the target sentences, while negative samples or the non-translations are numerous or unknown. We train models on four kinds of sampling sets with same translation characteristics and compare their performances. Experiments on the open dataset of 1500 pairs of English Chinese sentences are evaluated by three metrics with satisfying performances, much higher than the baseline system.
Cross lingual similarity discrimination with translation characteristics
Cross lingual similarity discrimination with translation characteristics
ijaia
Social media stats for 2013. Newest statistics on social media usage, social media trust and business value of social media from BuzzShaper lab. All the links, sources and surveys about social media statistics and numbers are named and listed -- http://buzzshaper.com
The newest stats on State of the Social media 2013. [buzzshaper]
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The buying and selling of video advertising is not new. A more recent development has been seen in the rise of the all-consuming publisher, and the amount of brands adopting video to target consumers across different channels. Then comes the tricky part: the measurement. In measuring the 'performance' of online video, what metrics should we be using to understand its impact? In this session, Head of International and Programmatic at Tremor Video, Greg Smith, will guide PMI attendees through the potential of video advertising and what advertisers, publishers and agencies really should be looking for when grading success.
Capture - Day 1 - 09:00 - "Drawing a Line Under the Measurement of Video Adve...
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acordero24
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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Sanusia1
Young2
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Web Search Engines are best gifts to the mankind by Information and Communication Technologies. Without the search engines it would have been almost impossible to make the efficient access of the information available on the web today. They play a very vital role in the accessibility and usability of the internet based information systems. As the internet users are increasing day by day so is the amount of information being available on web increasing. But the access of information is not uniform across all the language communities. Besides English and European languages that constitutes to the 60% of the information available on the web, there is still a wide range of the information available on the internet in different languages too. In the past few years the amount of information available in Indian Languages has also increased. Besides English and few European Languages, there are no tools and techniques available for the efficient retrieval of this information available on the internet. Especially in the case of the Indian Languages the research is still in the preliminary steps. There are no sufficient amount of tools and techniques available for the efficient retrieval of the information for Indian Languages. As we know that Indian Languages are very resource poor languages in terms of IR test data collection. So my main focus was mainly on developing the data set for URDU IR, training and testing data for Stemmer. We have developed a language independent system to facilitate efficient retrieval of information available in Urdu language which can be used for other languages as well. The system gives precision of 0.63 and the recall of the system is 0.8. For this Firstly I have developed an Unsupervised Stemmer for URDU Language [1] as it is very important in the Information Retrieval.
An unsupervised approach to develop ir system the case of urdu
An unsupervised approach to develop ir system the case of urdu
ijaia
In cross-lingual plagiarism detection, the similarity between sentences is the basis of judgment. This paper proposes a discriminative model trained on bilingual corpus to divide a set of sentences in target language into two classes according their similarities to a given sentence in source language. Positive outputs of the discriminative model are then ranked according to the similarity probabilities. The translation candidates of the given sentence are finally selected from the top-n positive results. One of the problems in model building is the extremely imbalanced training data, in which positive samples are the translations of the target sentences, while negative samples or the non-translations are numerous or unknown. We train models on four kinds of sampling sets with same translation characteristics and compare their performances. Experiments on the open dataset of 1500 pairs of English Chinese sentences are evaluated by three metrics with satisfying performances, much higher than the baseline system.
Cross lingual similarity discrimination with translation characteristics
Cross lingual similarity discrimination with translation characteristics
ijaia
Social media stats for 2013. Newest statistics on social media usage, social media trust and business value of social media from BuzzShaper lab. All the links, sources and surveys about social media statistics and numbers are named and listed -- http://buzzshaper.com
The newest stats on State of the Social media 2013. [buzzshaper]
The newest stats on State of the Social media 2013. [buzzshaper]
BuzzShaper
The buying and selling of video advertising is not new. A more recent development has been seen in the rise of the all-consuming publisher, and the amount of brands adopting video to target consumers across different channels. Then comes the tricky part: the measurement. In measuring the 'performance' of online video, what metrics should we be using to understand its impact? In this session, Head of International and Programmatic at Tremor Video, Greg Smith, will guide PMI attendees through the potential of video advertising and what advertisers, publishers and agencies really should be looking for when grading success.
Capture - Day 1 - 09:00 - "Drawing a Line Under the Measurement of Video Adve...
Capture - Day 1 - 09:00 - "Drawing a Line Under the Measurement of Video Adve...
PerformanceIN
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acordero24
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.
Detection of slang words in e data using semi supervised learning
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ijaia
There is distinct difference with iTicket POS, we connect directly with Disney's DTI removing the need for web portals when purchasing your Disney Theme Park Tickets.
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General Principles of Intellectual Property: Concepts of Intellectual Proper...
Poonam Aher Patil
In Bachelor of Pharmacy course, Class- 1st year, sem-II Subject EVS having topic of Energy Resources under the point Natural Resources. Following Presentation contain total information about the energy resources like Introduction of energy resources, Non-renewable, Renewable Energy resources with examples as well as deep explanations of coal, oil, petroleum, solar energy , wind energy , Conservation of Natural resources etc. Students having deep knowledge about energy resources after studying this presentation.
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
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General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
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