
{"publication_number": "US-6182026-B1", "abstract": "for translating a word - organized source text into a word - organized target text through mapping of source words on target words , both a translation model and a language model are used . in particular , alignment probabilities are ascertained between various source word & amp ; target word pairs , whilst preemptively assuming that alignment between such word pairs is monotonous through at least substantial substrings of a particular sentence . this is done by evaluating incrementally statistical translation performance of various target word strings , deciding on an optimum target word string , and outputting the latter .", "application_number": "US-10585598-A", "description": "the invention wants to translate a text , in particular a sentence that is written in a source language , into a sentence written in a target language . by way of non - limitative example , the disclosure infra , considers indogermanic languages such as french , spanish and english . inasmuch as these languages have a certain similarity in character , word - to - word translation has proven to be quite useful , but it is assumed that the procedures described herein will also have merit between languages of more distant character . now , a source sentence is f 1 j = f1 , f2 , . . . , fj , that may be translated into a target sentence e 1 i = e1 , e2 , . . . ei . among all possible target strings , the procedure followed herein will choose the one with the highest probability , which is given by bayesian decision rules according to : e 1 i = argmax ( e 1 i ){ pr ( e 1 i | f j 1 )