nlp_ie_13.py

# rule to extract initiative name
def sent_subtree(text):
    
    # pattern match for schemes or initiatives
    patterns = [r'\b(?i)'+'plan'+r'\b',
           r'\b(?i)'+'programme'+r'\b',
           r'\b(?i)'+'scheme'+r'\b',
           r'\b(?i)'+'campaign'+r'\b',
           r'\b(?i)'+'initiative'+r'\b',
           r'\b(?i)'+'conference'+r'\b',
           r'\b(?i)'+'agreement'+r'\b',
           r'\b(?i)'+'alliance'+r'\b']
    
    schemes = []
    doc = nlp(text)
    flag = 0
    # if no initiative present in sentence
    for pat in patterns:
        
        if re.search(pat, text) != None:
            flag = 1
            break
    
    if flag == 0:
        return schemes

    # iterating over sentence tokens
    for token in doc:

        for pat in patterns:
                
            # if we get a pattern match
            if re.search(pat, token.text) != None:

                word = ''
                # iterating over token subtree
                for node in token.subtree:
                    # only extract the proper nouns
                    if (node.pos_ == 'PROPN'):
                        word += node.text+' '

                if len(word)!=0:
                    schemes.append(word)

    return schemes      

# derive initiatives
df2['Schemes2'] = df2['Sent'].apply(sent_subtree)