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This paper presents a method for generating indexable and browsable keyword metadata from ASR transcripts by leveraging theWeb. Search engine queries are built from an ASR transcript and used to retrieve similar text from the Web. The keyword meta information embedded in those pages for search engines is then ranked
In recent years, blogs were used between friends and family members. Nowadays, more and more bloggers are willing to share information with others. Therefore, it is desirable that related blogs can be connected. Clustering is often used for establishing connections between blogs. Full-text keywords retrieval process
The field of Information Retrieval plays an important role in searching on the Internet. Most of the information retrieval systems are limited to the query processing based on keywords. In information retrieval system the matching of the query against a set of text record is the core of the system. Retrieval of the
Web-based mapping applications such as Google Maps or Virtual Earth have become increasingly popular. However, current map search is still keyword-based and supports a limited number of spatial predicates. In this paper, we build towards a natural language query interface to spatial databases to answer crime-related
query-keywords are used as a basis for sentence extraction. Results obtained from experiments performed have shown that such a combined approach can provide very interesting similarity calculation and re-ranking measure. This can be used with reasonable efficiency to detect duplications on search results generated by
Topic-oriented search engine (topic-search) is a new IR service which provides compounded types of information with certain user queried topic in one page. It firstly categorizes user query into a certain domain, and then organizes several types of information based on the query keywords into a magazine-style topic
their personal file system by leveraging semantic relationships available on the Web. More specifically, JabberWocky is using keyword/resource associations of social bookmarking web sites as a basis for recommending keywords for files. We chose social bookmarking web sites because of their popularity and because the
videos, we can only use a title. If there are tags - significant keywords of that multimedia, we can use tag information to search. Tag is a keyword of text, blog post, or multimedia. Users have already recognized about the value and importance of tags but only a few users are using tags. They might be annoying to add tags
of any typical search engine by either restricting the results to matching categories or enriching the query itself. The presented method effectively rules out noise words within a query, forms the optimal keyword packs using a density function, and returns a set of category labels which represent the common topics of
In this paper, we examine the significance of expansion of the user query by two techniques namely Efficient Clustering-By-Direction and Theme Clustering. These two techniques produce the clusters of keywords extracted from the set of retrieved documents for the user query. The former clustering is based on
Tagging refers to the metadata that many users added in the form of keywords on photos, videos, and other resources for sharing the contents via the internet. However, there are several difficulties with tagging that come from tag variation, tag ambiguity and flat organization. This paper presents the integration of
model in the data set of flickr. The final ranked pictures are the combination of keywords and users' preference matching. The experiment proves that our method is better than both non-personalization method and common personalization method.
gets answer in the form of compact text along with map for visualization purpose. This aims at developing geographic domain question answering system [QAS] embedded with mapping abilities where our system will allow users to specify a location addition to the keywords they are searching for as a query. Our system will
Folksonomy systems enable users to participate in the Web content creation process by annotating (tagging) resources with freely chosen keywords. Still, it is an open issue how to exploit this user-created content, and how to process and use these emergent semantics effectively. We investigate how the context of Web
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