Search results
Results from the WOW.Com Content Network
In linguistics, lexical similarity is a measure of the degree to which the word sets of two given languages are similar. A lexical similarity of 1 (or 100%) would mean a total overlap between vocabularies, whereas 0 means there are no common words. There are different ways to define the lexical similarity and the results vary accordingly.
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. ROUGE metrics range between 0 and 1, with higher scores indicating higher similarity between the automatically produced summary and the reference.
The Automated Similarity Judgment Program (ASJP) is a collaborative project applying computational approaches to comparative linguistics using a database of word lists. The database is open access and consists of 40-item basic-vocabulary lists for well over half of the world's languages. [ 1 ]
Version 0.2 of October 2016, supports the algorithms BWA-MEM, BWA-backtrack, and BWA-ALN. All of them work with single-reads and paired-end reads. Yes Low quality bases trimming Yes Yes Free, GPL 3 [57] 2016 SSAHA, SSAHA2 Fast for a small number of variants Proprietary, freeware for academic and noncommercial use Stampy For Illumina reads.
Data can be binary, ordinal, or continuous variables. It works by normalizing the differences between each pair of variables and then computing a weighted average of these differences. The distance was defined in 1971 by Gower [1] and it takes values between 0 and 1 with smaller values indicating higher similarity.
The higher the Jaro–Winkler distance for two strings is, the less similar the strings are. The score is normalized such that 0 means an exact match and 1 means there is no similarity. The original paper actually defined the metric in terms of similarity, so the distance is defined as the inversion of that value (distance = 1 − similarity).
Calculate a similarity score that is the sum of the joined regions penalising for each gap 20 points. This initial similarity score ( initn ) is used to rank the library sequences. The score of the single best initial region found in step 2 is reported ( init1 ).