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
This is using glob rather than listdir, but this might be a possible method. No regular expressions involved here either though. import glob folder_path = “C:\Temp” file_pattern = “\*.txt” search_string = “hello” match_list = [] folder_contents = glob.glob(folder_path + file_pattern) for file in folder_contents: print(“Checking”, file) read_file = open(file, ‘rt’).read() if search_string in read_file: match_list.append(file) … Read more
There is not one single algorithm for extraction based summarization. There are several different algorithms to choose from. You should choose one that fits your specific needs. There are two approaches to extraction based summarization: Supervised learning – you give the program lots of examples of documents together with their keywords. The program learns what … Read more
You can get it working to some extent with pyenchant library that allows checking if a word is a valid word in a given language. Before checking linguistic validity, you need to check if the word is not empty and is more than one char long the word consists of letters only. So, in Python, … Read more
string= “machine learning”.split() doc_vector = model.infer_vector(string) out= model.docvecs.most_similar([doc_vector]) I’m not sure 100% since I’m using a more recent release, but I think that the issue is connected to the fact that the most_similar function is expecting a string mapped in the feature space and not the raw string. 2 solved Gensim example, TypeError:between str and … Read more
Based on the expected output, you want more than just digits. The digits can have /, – and ,. Maybe, perhaps, you wanted something that starts with and ends with a digit, but anything in the middle other than a space? import re a=”invoice β invoice # 2018-33167-2 β date 03/21/2018 β total due $8,804.90″ … 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) solved Machine understand natural language – nlp [closed]