Sentence examples for topic we used from inspiring English sources

"We could pick a topic — we used to share articles all the time, with Google Reader".

Footnotes:* He was probably referring to the World Bank's calculation for 2005, as featured in this paper by Martin Ravallion and Shaohua Chen (see Table 2).** In our previous posts on this topic, we used national inflation not rural inflation.*** This PPP is based on the results of a 2011 comparison of worldwide prices, published earlier this year.

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).

To shed light on this old topic, we used a new approach by considering an OLG model populated by households that are heterogeneous by level of education, who optimally choose the consumption of market- and home-produced goods, and the time spent working in the market and in home production.

To elicit the men's beliefs and experiences on this sensitive topic we used semistructured face to face interviews.

In order to find all available publications on the topic, we used a three-stage search technique and minimised language limitations.

In order to forecast future trend of the topic, we use the following benchmark methods: Using All, Highest Correlated field, Highest Correlated field plus one.

53 54 To evaluate the quality of evidence with specific regard to genetic topics, we used the Venice consensus criteria 43 for rating the cumulative epidemiological evidence of meta-analyses of genetic association studies that yield significant (P<0.05) summary estimates.

Since stakeholders are likely to have contrasting views about these topics, we used stakeholder analysis [ 20] to collect and analyze information about discourses and mobilization on health rights among general and disease-specific stakeholders in Peru.

In order to find a set of topics which are strongly correlated to each other (or strongly inversely correlated topics), we use correlation co-efficient between two topics to build a graph.

We applied the unsupervised learning method LDA varying the number of topics n over the dataset L. In order to form the positive instances of topic i we used a threshold of κ1 = 0.02 and to form the negative instances of topic i we used a threshold of κ2 = 0. LDA was applied to a collection of documents in the training set L which contain only the PICO portions of the original document.