Sentence examples for Adjacent words from inspiring English sources

Some of the lists were designed to encourage the exchange of onset consonants between adjacent words.

They were mostly definitions of words, both words from the text and adjacent words from the Arabic-English dictionary which had caught Teyeb's eye: not just "perpetuate" but also a little treatise on "peripatetic"; not just "veteran" but also "vex".

Our method uses no a-priori segmentation of the text, and can mark boundaries anywhere (i.e. between any adjacent words), although it is more likely to do so at natural boundaries such as sentences and paragraphs.

However, such an approach fails to exploit the fact that the classifications of adjacent words are interdependent.

Phrase searching restricts the search to adjacent words in a particular order (punctuation in the text, except for apostrophes, should not be entered).

N-grams were generated by inclusion of pairs (bigrams) and triplets (trigrams) of adjacent words; in these cases stop words or connecting words were kept.

Landing position distributions can be approximated by normal distributions, however, these distributions are truncated at word boundaries, suggesting that some of the fixations observed experimentally on a particular word were in fact intended for an adjacent word [3].

Results: To reduce the statistical dependency between adjacent word matches, we propose to use 'spaced words', defined by patterns of 'match' and 'don't care' positions, for alignment-free sequence comparison.

Crucially, the syntactic violation relied on a structural syntactic dependency between two non-adjacent words in the sentence, whereas the semantic violation relied on purely lexical semantic restrictions of the preceding verb.

(1) Query formation The query is formed from the example sentences of the given word sense in the WordNet dictionary by taking the target word and its adjacent two words on both sides after removing stop words.

(3) Snippet scoring and selection Three scoring criteria were used to choose the most probable snippet including the target word used in the given word sense: (1) the word overlap between the example sentence and the snippet, (2) the number of adjacent query words to the target words in the snippet, and (3) the number of query words that appear in the snippet.