SPEECH CONCLUSION PROCESSING

conclusion.py

+import sys
+from operator import index
+import spacy
+import textacy
+
+#Identify words that are in a introduction
+
+
+nlp = spacy.load("en_core_web_sm")
+
+s = " where does he go few moments ago he went once upon a time asdasd  dwref ADSADSADA Hello i'm Gabi :D  he asked me to do it i said good bye to him he replyed fot itgoodbye asd asl a he started running. once upon a  time he went to the let me briefly temple."
+
+
+totalCharacterCount = len(s)
+print(totalCharacterCount)
+
+conclusionCharacterCount = (85/100)*totalCharacterCount
+print(conclusionCharacterCount)
+
+conclusion = (s[int(conclusionCharacterCount):int(totalCharacterCount)])
+print(conclusion)
+
+final_words = []
+
+words = ["according to a study", "according to a research","according to a review","according to a survey","according to an analysis","according to one study","according to research","according to an investigation","let me briefly"]
+for wordPharse in words:
+    if wordPharse in conclusion:
+        final_words.append(wordPharse)
+print(final_words)
+
+doc = nlp(conclusion)
+
+patterns = [[{"POS": "ADV"}, {"POS": "AUX"}, {"POS": "PRON"}], [{"POS": "ADV"}, {"POS": "AUX"}, {"POS": "NOUN"}]]
+
+verb_phrases = textacy.extract.token_matches(doc, patterns)
+
+# print(s[0:introductionCharacterCount])
+
+for verb_phrases in verb_phrases:
+    print(f"Identified questions : {verb_phrases}")
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