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The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these
Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. As one common way for evasion, the merchants and buyers together create a vocabulary of jargons (called "black keywords" in this
-processing of Web search results have been extensively studied to help user effectively obtain useful information. This paper has basically three parts. First part is the review study on how the keyword is expanded through truncation or wildcards (which is a little known feature but one of the most powerful one) by using
analysis and results ranking. For semantic annotation, we use domain ontology and two bilingual dictionaries to extract keywords for annotation. For query analysis, we present a method which combines lexical relationship and semantic relationship to analyse user's query. And for results ranking, we propose a modulative method
neighbor search of videos from Internet. The fundamental problem lies on the scalability of a search technique, in face of the intractable volume of videos which keep rolling on the Web. In this paper, we investigate scalability of several well-known features including color signature and visual keywords for Web-based
relational database of web pages. So there are many researches focusing on the search in these relational database with keywords, compared with these researches, our algorithms are mainly based on bags using the greedy algorithms and supporting the phrase recognition by utilizing multiple dictionaries. We make a comparison
Term ambiguity — the challenge of having multiple potential meanings for a keyword or phrase — can be a major problem for search engines. Contextual information is essential for word sense disambiguation, but search queries are often limited to very few keywords, making the available textual context needed for
in both Thai and English is built for helping users from a lot of keywords of the same term and (3) a set of keywords from herbal usages can be combined with the name keyword. From the results, information collected from KUIHerb is useful for searching.
this job fairly well, but does not perform as well when the feedback information is only about non-relevant webpages. In such an unfavorable circumstance the legwork mechanism assists the suggesting module by handing over the relevant information after finding one or more webpages, containing at least one clue keyword
taken into account when indexing documents and when performing searching. Utilizing this approach, it is possible to use a natural language to express user queries. In many cases, this way is more usual for users to describe their information needs compared to the keyword style. The factoid question answering task is one
: the morphological, syntactic, and semantic levels. The basic unit of our search engine is the meaning not the word structure as structural research engines. It operates by keyword, subject, and syntactic analysis. We have developed a program that will be a search engine in the texts of the Holy Quran. ()
propose n keywords, in order to optimise the information gain expectation. Its implementation, CFAsT, endeavours to keep the best from both worlds: the universality and automatic generation from search engines, and the usability, the assistance and the self optimisation provided by the dialogue systems. Thus, a beta dialogue
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.