detecting english programmatically Algorithm
The detecting English programmatically algorithm is an intelligent system designed to identify and differentiate the English language from other languages within a given text or document. This algorithm is built on the foundation of natural language processing, machine learning, and linguistics, which collectively work together to analyze and recognize the specific patterns, syntax, and semantics that are unique to the English language. The primary goal of this algorithm is to automatically and accurately detect English text within a larger body of text or in a standalone format, enabling efficient language identification, filtering, and processing.
To achieve this objective, the algorithm utilizes various techniques and approaches to analyze the text, including the evaluation of character frequencies, the identification of common English words, and the analysis of linguistic structures. By examining the frequency and distribution of characters and words within the text, the algorithm can effectively determine the likelihood of the content being in English. Moreover, by analyzing the syntactic and semantic patterns of the text, the algorithm can further validate its language identification. This multi-layered approach enables the detecting English programmatically algorithm to achieve higher accuracy and reliability in identifying English text, which in turn facilitates better language processing, translation, and analysis applications.
import os
UPPERLETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
LETTERS_AND_SPACE = UPPERLETTERS + UPPERLETTERS.lower() + " \t\n"
def loadDictionary():
path = os.path.split(os.path.realpath(__file__))
englishWords = {}
with open(path[0] + "/dictionary.txt") as dictionaryFile:
for word in dictionaryFile.read().split("\n"):
englishWords[word] = None
return englishWords
ENGLISH_WORDS = loadDictionary()
def getEnglishCount(message):
message = message.upper()
message = removeNonLetters(message)
possibleWords = message.split()
if possibleWords == []:
return 0.0
matches = 0
for word in possibleWords:
if word in ENGLISH_WORDS:
matches += 1
return float(matches) / len(possibleWords)
def removeNonLetters(message):
lettersOnly = []
for symbol in message:
if symbol in LETTERS_AND_SPACE:
lettersOnly.append(symbol)
return "".join(lettersOnly)
def isEnglish(message, wordPercentage=20, letterPercentage=85):
"""
>>> isEnglish('Hello World')
True
>>> isEnglish('llold HorWd')
False
"""
wordsMatch = getEnglishCount(message) * 100 >= wordPercentage
numLetters = len(removeNonLetters(message))
messageLettersPercentage = (float(numLetters) / len(message)) * 100
lettersMatch = messageLettersPercentage >= letterPercentage
return wordsMatch and lettersMatch
if __name__ == "__main__":
import doctest
doctest.testmod()
