find_target.py

#==============================================================================
#TARGET NAME EXTRACTOR
#==============================================================================

# import relevant modules and data
#==============================================================================
import re
from csv import reader

#start list of to store target occurences
target_list=['\t'.join(['docid','sentid','target','start_idx','end_idx','adjectives','sentence'])]

#define target word(s)
with open('./var/target_variables.txt') as fid:
    target_variables = fid.readlines()

for i in target_variables:
    exec i

#matching is case insensitive
target_names=[t.lower() for t in target_names]

#load and loop through data
with open('./input/sentences_nlp352','r') as fid:
    for r in fid:
        #file is table delimited
        row=r.replace('\n','').split('\t')
        #first three items are integers/strings        
        docid=row[0]
        sentid=row[1]
        tmp=row[2:]

        #the rest are comma-delimited lists
        wordidx=[l for l in reader([tmp[0][1:-1]])][0]
        wordidx=[int(w) for w in wordidx]
        words=[l for l in reader([tmp[1][1:-1]])][0]
        poses=[l for l in reader([tmp[2][1:-1]])][0]
        ners=[l for l in reader([tmp[3][1:-1]])][0]
        lemmas=[l for l in reader([tmp[4][1:-1]])][0]
        dep_paths=[l for l in reader([tmp[5][1:-1]])][0]
        dep_parents=[l for l in reader([tmp[6][1:-1]])][0]
        dep_parents=[int(d) for d in dep_parents]

        #build list of target occurences for this document
        targets = []
    
        #sentence string
        sent = ' '.join(words)
    
        #loop through all the target names
        for name in target_names:
            #starting index of all matches for a target_name in the joined sentence
            matches=[m.start() for m in re.finditer(name,sent.lower())]

            if matches:
        	    #if at least one match is found, count number of spaces backward to arrive at word index
        	    indices = [sent[0:m].count(' ') for m in matches]
        	    #remove double hits (i.e. stromatolitic-thrombolitic)
        	    indices = list(set(indices))
        	    #target_name spans its starting word index to the number of words in the phrase
        	    target_word_idx = [[i,i+len(name.split(' '))] for i in indices]
        
        	    #initialize other data about a found target_name
        	    target_pose=[]
        	    target_path=[]
        	    target_parent=[]
        	    target_adj=[]

        	    for span in target_word_idx:
                     #poses, paths and parents can be found at same indices of a target_name find
                     target_word = ' '.join(words[span[0]:span[1]])

                     if target_word.lower() not in bad_words:
                         target_children=[]
                         target_pose = poses[span[0]:span[1]]
                         target_path = dep_paths[span[0]:span[1]]
                         target_parent = dep_parents[span[0]:span[1]]

            		   #children of each component of a target_name
                         for span_idx in range(span[0], span[1]):
                             children = [j for j,i in enumerate(dep_parents) if i==span_idx+1]
                             target_children.append(children)
                             
                             #gather adjectives of target word
                             for c in children:
                                 if poses[c]=='JJ':
                                     target_adj.append(words[c])
                             
            		   #convert parent_ids to Pythonic ids
                         target_parent = [i-1 for i in target_parent]

            		   #add finds to a local variable
                         target_list.append('\t'.join([docid, sentid, target_word, str(span[0]),str(span[1]), str(target_adj), sent]))

#write the output as tab-separated values
with open('./output/output.tsv', 'w') as f:
    f.write('\n'.join(target_list))