Sentence examples for discover topics from inspiring English sources

The technique has been employed both to discover topics in text collections and to structure document sets for advanced searching.

In this paper, we propose to use sentence-level association rule mining to discover topics from documents.

The goal is to help users discover topics that they'll be interested in quickly, and then foster productive conversation.

To discover topics, we converted each sentence of the document into a transaction format, and employed association rule mining algorithms to discover frequent patterns from the documents.

Researchers explore such data to discover topics of interest, find related research groups, and estimate the impact of authors and publications [1 6].

This 'Joint' ranking method coupled with an unsupervised topic clustering model is shown to have the potential to discover topics of interest or concern to a local community.

4 Topic correlation Calculating correlations between discovered topics.

The number of topics, K, is a user-specified parameter that provides control over the level of details of the discovered topics.

The discovered topics then help to predict missing tags of an unseen image as well as the ones partially labeled in the database.

The results show that out of the 54 discovered topics, only four of them (marked in italic format) are not meaningful topics and the remaining 50 are legitimate topics.

While mining topics in a document collection, in order to capture the relationships between words and further improve the effectiveness of discovered topics, this paper proposed a feedback recurrent neural network-based topic model.