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In many cases keywords from a restricted set of possible keywords have to be assigned to texts. A common way to find the best keywords is to rank terms occurring in the text according to their tf.idf value. This requires a corpus of texts from which document frequencies can be derived. In this paper we show that we
Query-recommendation systems based on inputted queries have become widespread. These services are effective if users cannot input relevant queries. However, the conventional systems do not take into consideration the relevance between recommended queries. This paper proposes a method of obtaining related queries and clustering them by using the history of query frequencies in query logs. We define...
this problem by automatically dividing the social network of a Twitter user into personal cliques, and annotating each clique with keywords to identify the common ground of a clique. Our proposed clique annotation method extracts keywords from the tweet history of the clique members and individually weights the extracted
FCA, a session interest concept is defined as a pair of extent and intent where the extent covers a set of documents selected by the user among the search results and the intent covers a set of keyword features extracted from the selected documents. And, in order to make a concept network grow, we need to calculate the
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