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In the case of English-French lexical similarity, at least two other studies [7] [8] estimate the number of English words directly inherited from French at 28.3% and 41% respectively, with respectively 28.24% and 15% of other English words derived from Latin, putting English-French lexical similarity at around 0.56, with reciprocally lower ...
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.
Pairwise comparison of profile Hidden Markov models; very sensitive: Protein: Söding J [7] [8] 2005/2012 IDF Inverse Document Frequency: Both: Infernal Profile SCFG search: RNA: Eddy S: KLAST High-performance general purpose sequence similarity search tool: Both: 2009/2014 LAMBDA High performance local aligner compatible to BLAST, but much ...
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 ]
However, parser generators for context-free grammars often support the ability for user-written code to introduce limited amounts of context-sensitivity. (For example, upon encountering a variable declaration, user-written code could save the name and type of the variable into an external data structure, so that these could be checked against ...
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 ).
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).