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| import spacy | |
| import pytextrank | |
| import re | |
| from operator import itemgetter | |
| class KeywordExtractor: | |
| def __init__(self): | |
| self.nlp = spacy.load("en_core_web_sm") | |
| self.nlp.add_pipe("textrank") | |
| def get_keywords(self, text, max_keywords): | |
| doc = self.nlp(text) | |
| kws = [i.text for i in doc._.phrases[:max_keywords]] | |
| return kws | |
| def get_keyword_indicies(self, string_list, text): | |
| out = [] | |
| for s in string_list: | |
| indicies = [[m.start(), m.end()] for m in re.finditer(re.escape(s), text)] | |
| out.extend(indicies) | |
| return out | |
| def merge_overlapping_indicies(self, indicies): | |
| # Sort the array on the basis of start values of intervals. | |
| indicies.sort() | |
| stack = [] | |
| # insert first interval into stack | |
| stack.append(indicies[0]) | |
| for i in indicies[1:]: | |
| # Check for overlapping interval, | |
| # if interval overlap | |
| if (stack[-1][0] <= i[0] <= stack[-1][-1]) or (stack[-1][-1] == i[0]-1): | |
| stack[-1][-1] = max(stack[-1][-1], i[-1]) | |
| else: | |
| stack.append(i) | |
| return stack | |
| def merge_until_finished(self, indicies): | |
| len_indicies = 0 | |
| while True: | |
| merged = self.merge_overlapping_indicies(indicies) | |
| if len_indicies == len(merged): | |
| out_indicies = sorted(merged, key=itemgetter(0)) | |
| return out_indicies | |
| else: | |
| len_indicies = len(merged) | |
| def get_annotation(self, text, indicies, kws): | |
| # Convert indicies to list | |
| # kws = kws + [i.lower() for i in kws] | |
| arr = list(text) | |
| for idx in sorted(indicies, reverse=True): | |
| arr.insert(idx[0], "<kw>") | |
| arr.insert(idx[1]+1, "XXXxxxXXXxxxXXX <kw>") | |
| annotation = ''.join(arr) | |
| split = annotation.split('<kw>') | |
| final_annotation = [(x.replace('XXXxxxXXXxxxXXX ', ''), "KEY", "#26aaef") if "XXXxxxXXXxxxXXX" in x else x for x in split] | |
| kws_check = [] | |
| for i in final_annotation: | |
| if type(i) is tuple: | |
| kws_check.append(i[0]) | |
| return final_annotation | |
| def generate(self, text, max_keywords): | |
| kws = self.get_keywords(text, max_keywords) | |
| indicies = list(self.get_keyword_indicies(kws, text)) | |
| if indicies: | |
| indicies_merged = self.merge_until_finished(indicies) | |
| annotation = self.get_annotation(text, indicies_merged, kws) | |
| else: | |
| annotation = None | |
| return annotation, kws | |