Sentence examples for discovering topics from inspiring English sources

Bernstein et al. [9] developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation, and then the tweets can be grouped into topics mentioned explicitly or implicitly.

Since we were focusing on discovering topics from daily MBPs with statistical analysis, rather than on a specific user or a specific topic, we used the public-timeline interface [3] to collect MBPs (see Figure 1 for an example of such interface).

However, there are two critical challenges in discovering topic time from Web news pages.

Aiming at solving these two problems, we propose a systematic framework for discovering topic time from Web news.

4 Topic correlation Calculating correlations between discovered topics.

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.

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

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.