I’d do it in several steps: Split on logical operators into separate inequalities. Change double inequalities like -3>x1>=-1.45 into two inequalities. Change “=” to “==”, and put it all together. For example: a1 <- strsplit(a, “&”, fixed = TRUE)[[1]] a1a <- gsub(” “, “”, a1) # get rid of spaces a2 <- gsub(“([-0-9.]+[<>=]+)([[:alpha:]]+[[:alnum:]]*)([<>=]+.+)”, “\\1\\2 & … Read more
There are plenty of them. Here is a list of different NLP problems: spam detection text genre categorization (news, fiction, science paper) finding similar texts (for example search for similar articles) find something about author (genre, native-speaker/non-native-speaker) create automatic grader for student’s work check text for plagiarism create an application that looks for grammatical errors … Read more
Check these out in that order 🙂 Natural Language Tool Kit [NLTK] main page(It is in Python),Building a machine learning class around NLTK And Making an actual Application out of it. This should give you a great foundation for building NLP applications(provided your NLP fundamentals are strong)
Something like the following will work: with open(‘resume.txt’, ‘r’) as f_input: print re.findall(r’\b([a-z0-9-_.]+?@[a-z0-9-_.]+)\b’, f_input.read(), re.I) It will display: [‘[email protected]’] But the exact logic can be far more complicated. It all depends on how accurate it needs to be. This will display all email addresses in the text file, just in case there is more than … Read more