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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 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 ]
By the original design the GDT algorithm calculates 20 GDT scores, i.e. for each of 20 consecutive distance cutoffs (0.5 Å, 1.0 Å, 1.5 Å, ... 10.0 Å). [2] For structure similarity assessment it is intended to use the GDT scores from several cutoff distances, and scores generally increase with increasing cutoff.
2. Sentence: S 3. Verb phrase: VP 4. Clause: C 5. T-unit: T 6. Dependent clause: DC 7. Complex T-unit CT 8. Coordinate phrase CP 9. Complex nominal CN 10. Syntactic complexity indices Length of production units Mean length of sentence MLS 11. Mean length of T-unit MLT 12. Mean length of clause MLC 13. Overall sentence complexity Clause per ...
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).
The Coleman–Liau index is a readability test designed by Meri Coleman and T. L. Liau to gauge the understandability of a text. Like the Flesch–Kincaid Grade Level, Gunning fog index, SMOG index, and Automated Readability Index, its output approximates the U.S. grade level thought necessary to comprehend the text.
Similarity scores are among the many original sabermetric concepts first introduced by Bill James. James initially created the concept as a way to effectively compare non- Hall of Fame players to players in the Hall, to see who was either on track to make the HOF, or to determine if any eligible players had been snubbed by the selection committee.