Sentence examples for clustered words from inspiring English sources

I am in bed with a book, naked, and hardly able to focus on the clustered words and rigid descending paragraphs, because Maureen is in the bathroom slipping into the sheer black negligee I bought her at Victoria's Secret for her birthday, and her every sound — the creak of the medicine cabinet on its hinges, the tap running, the susurrus of the brush at her teeth — electrifies me.

Or if there are a lot of clustered words that focus on "teeth", this may be another way to approach "a leopard".. Answer questions like: "I was surprised by…" or "I discovered…" For example, you may look over the clustered words and note "I was surprised by how often I mention my brother in relation to a leopard".

Or if there are a lot of clustered words that focus on "flowers, this may be another way to approach "Spring".. Answer questions like: "I was surprised by…" or "I discovered…" For example, you may look over the clustered words and note "I was surprised by how often I mention my mother in relation to Spring".

We also show that although stripe CRMs can be recovered by identifying clustered word profiles, neighboring dissimilar word profiles are a more common feature of regulatory sequences.

In this part, we cluster words with the same meaning together before calculating sentiment.

This supported the idea that epigenetic marks can be helpful to cluster words [ 4].

The advantage of clustering PFMs compared with clustering words (as in RSAT) is that PFMs contain more information than words alone.

Here we use a similar approach as CisFinder [ 17] which clusters words by PFMs since PFMs contain more site-specific details about each motif.

Since topic modeling is designed to cluster words co-occurring frequently across whole documents, genes with a high rank in a topic are presumably involved in the biological processes determined by that topic.

The statistically interesting words in each segment were clustered with similar words to generate motif logos.

We design a top-down divisive clustering algorithm that ensures maximal information gain in the use of unlabeled data via clustering similar words.