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In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted
A data element specifies one of the characteristics of its parent element. Therefore, the context of a data element is determined by its parent. Non context driven search engines build relationships between data nodes based solely on their labels and proximity to one another while overlooking their contexts. Therefore, they may return faulty answers. This paper investigates the pitfalls and limitations...
In search engine advertising, the right selection of keywords is one of the most important step to maximize the advertisers' profitability. Keyword suggestion methodologies are used to map the advertisers selection of keywords and the recent popular queries using statistical data. In this project we are trying to
approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a
As personalization technologies are widely used, preference extraction is becoming important. In this work, we propose a preference extraction method on the basis of applications that are installed on a user's smart device. In this method, keywords are extracted from descriptions of the installed applications on an
A new context-based model (CoBAn) for accidental and intentional data leakage prevention (DLP) is proposed. Existing methods attempt to prevent data leakage by either looking for specific keywords and phrases or by using various statistical methods. Keyword-based methods are not sufficiently accurate since they ignore
Current search engine performances need to be improved because often the result suggested by search engine are determine the popularity of a given page for its associated keywords but does not match specific user expectations. Previous researches have indicated that only 20% to 45% of the common search results are
align two keywords respectively, while KWIC aligns one keyword. This helps to find collocations among words. Furthermore, KWISC is able to expand and collapse bunsetsus. It can shorten distances of collocating words since it shows only the main structure of sentences by collapsing the bunsetsus. Therefore, collocating
When using Information Retrieval (IR) systems, users often present search queries made of ad-hoc keywords. It is then up to the information retrieval systems (IRS) to obtain a precise representation of the user's information need and the context (preferences) of the information. To address this problem, we investigate
The intelligent help system (IHS) is an important assistant platform, especially in online teaching system. At present most search methods of the help systems are mainly based on keyword matches in database query or hierarchical classifications. The outstanding problem is that users are required to have a certain
, tornadoes, earthquakes), or incidents (e.g., traffic jams). However, current search on social media data is mostly keyword or hashtag based. The keyword based search method does not allow efficient search of events. In order to detect, scan, and search location based events from social media, users and social texts need to be
order to eliminate noisy data when matching, the boundaries of candidates are relocated by the presented method named forward-backward keyword matching based on the corpus from People's Daily. Experimental results on Sogou corpus indicate that the trend selection method is better while compared to other template selection
Traditional web search forces the developers to leave their working environments and look for solutions in the web browsers. It often does not consider the context of their programming problems. The context-switching between the web browser and the working environment is time-consuming and distracting, and the keyword
heterogeneity where diverse advertisers of different types may be competing for the same search keyword. We examine advertisers' behaviors from the lens of the strategic group theory. We first develop an approach to cluster participating advertisers into strategic groups and then empirically examine whether these groups manifest
We introduce in this paper a system called EDGT, which determines the semantic relationships among Gene Ontology terms. EDGT accepts Keyword-based queries with the form Q (“t1”, “t2”, ‥, “tn”) and Loosely Structured queries with the form
As more web services are offered on the Web, it is becoming increasingly difficult for users to manage and search for online content, using only flat keyword searching. Users often forget how they tagged their data but may remember generic information such as the location they were in when they took the picture. We
rank candidate APIs using popularity (a social measure) or keyword-based measures (whether semantic or unverified tags). This article proposes to use information on co-usage of APIs in previous mash ups to suggest likely candidate APIs, and introduces a global measure which improves on earlier local co-API measures. The
search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result
main current approaches for semantic discovery of services are the keyword-based approach and the ontology-based approach. The plain simple keyword matching strategy is time-consuming and has inefficient recall and precision. The ontology-based strategy, on the other hand, is efficient, but may not be practical for the
consider information that is contextually similar to information related to a particular topic as it provides a big picture. Tweets contains keywords known as hashtags which provide useful information for the purpose of sentiment analysis, named entity recognition, event detection, etc. In this paper, we have analyzed Twitter
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