Sentence examples for sentence filtering from inspiring English sources
Coverage and anti-redundancy is achieved with the help of sentence filtering while generating the final summary.
In the last stage, sentence filtering is performed from each individual input text document on the basis of frequent and semantic similar terms generated from previous stage.
This tool, consisting on an article filter followed by a sentence filter, has been trained on the BioCreAtIvE II workshop dataset, enriched by other selected known-interactions.
Using a 10-fold cross validation and 0.5 probability cutoff, the method showed a precision of 87.4% for the article filter, and 92.1% for the sentence filter.
Semantic classes including the adopted and rejected class derived from the training set using semantic-orientation point-wise mutual information (SO-PMI) are used for selecting potential sentences and filtering out FP sentences (67).
Linguistic changes identified by the research so far include differences in use of pronouns and multiple negatives, based on whether Welsh was learnt in the home or at school — with English sentence constructions filtering into the language more when Welsh is learnt at school vs at home.
Then all the sentences are processed with the MetaMap for Named Entity Recognition (NER) and to limit the semantic types of initial terms and linking terms in a sentence, we filter out the sentences which contain the concepts of the unneeded semantic types.
Rather, reaction to the trial and the sentence is filtered through the prism of Muslim suspicions that Americans are biased against them and their religion.
The extraction module looks for words and phrases common among the highly scored sentences, filters out known irrelevant phrases, and outputs the rest as the target information.
As a final step, to select a set of putative biomarker genes from our gene dictionary, we conducted a text mining search for the genes that are mentioned together with biomarker terms in the same sentence (biomarker rule filtering) and then all entries of this set of genes were extracted from the dictionary as putative biomarkers-related terms.
We relied on simple and strong assumptions for building a training set from the official data, and used a simple naïve Bayes classifier for filtering sentences.
