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by the network -- descriptive keywords, or tags. In this paper we present a model that enables keyword discovery methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid
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
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
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
Collaborative tagging systems have recently emerged as a powerful way to label and organize large collections of data. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find
In this paper, cascade Chinese potential name recognition is proposed. Internal information of a person like family name, first name do not needed for name recognition while context keywords is used for name guessing. Some conceptions such as bidirectional potential name recognition, rough confirmation of potential
The scale of the social web has integrated users in order to organize shared resources. Users freely associate keywords (tags) to resources. This collection of tags creates a folksonomy. Folksonomy is a collaborative tagging system, which has grown popular with its simplicity of free tagging. However, it rises up a
make tweets semi-structured inside and semantically related to each other. Since hashtags are utilized as keywords in tweets to mark messages or to form conversations, they provide an additional path to connect semantically related words. In this paper, treating tweets as semi-structured texts, we propose a novel topic
Keywords or tags summarize documents on an abstract level and can also be used for describing code fragments. They might be leveraged for retrieving features of a software system, understanding program functionality, or providing additional context. While automatic approaches at best are only able to retrieve
searching. The distribution of tag types differs greatly across different systems. Also the distribution shows large difference between publishers and searchers. In order to expand tags of resources for publishers and keywords for searchers reasonable, this paper shows a comparison of the distributions of both kinds of users
The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore
to collect active commercial entities and a commercial relation lexicon is created to collect keywords that flag commercial relations. Illustration and applications are also discussed, which undoubtedly discloses a promising future of commercial network study.
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