def parse_file(file):
tree = ET.parse(file)
root = tree.getroot()
mails = []
space = re.compile(r' ',re.S)
sp = re.compile(r'\-+',re.S)
gt = re.compile(r'\>',re.S)
for i in root:
tmp = mail_struct(len(mails))
for j in i:
if(j.tag == 'name'):
#print(space.sub('_',j.text))
tmp.name = space.sub('_',j.text)
tmp.remove_stop_ver_title = stop_word_remove(j.text)
#print(j.text)
elif(j.tag == 'DOC'):
thread_num = -1
for k in j:
if k.tag == 'Subject':
tmp.subject.append(k.text)
if(k.tag == 'Text'):
for l in k:
res = gt.search(l.text)
'''
if(res is not None or l.text[0] == '>' or(l.text[0] =='_' and l.text[1] == '__')):
continue
if(l.text[0] == 'h' and l.text[1]=='t' and l.text[2] == 't' and l.text[3] == 'p'):
continue
'''
tmp_text = l.text.replace('\n',' ')
tmp_text = sp.sub('',tmp_text)
if(tmp_text.isspace()):
continue
t_s = sentence_struct(tmp_text,l.attrib['id'])
tmp.text.append(t_s)
if t_s.index.split('.')[0] != thread_num:
thread_num = t_s.index.split('.')[0]
tmp.thread.append(thread_struct(int(thread_num)))
#thread_num = t_s.index.split('.')[0]
tmp.thread[-1].sentences.append(t_s)
#print t_s.sentence
#tmp.id.append(l.attrib['id'])
#print(l.text)
mails.append(tmp)
for i in mails:
for j in i.text:
for k in range(len(j.remove_stop_ver)):
if(len(j.remove_stop_ver[k])<2 or j.remove_stop_ver[k] == 'gt' or j.remove_stop_ver[k] == 'gtgt'):
j.remove_stop_ver[k] = ''
#print(j.remove_stop_ver)
j.remove_stop_ver = (filter(None,j.remove_stop_ver))
return mails
def process_document(title,sen2=1,exist=1):
article,art_title = crawl_article(title)
#sen2 = 1
#exist = 1
if(sen2 == 1):
input_name = title+'_sen2_test.txt'
feature_name = title+'_sen2_features.txt'
output_name = title+'_sen2_output.txt'
vec = construct_2sen_vec(article)
else:
input_name = title+'_test.txt'
feature_name = title+'_features.txt'
output_name = title+'_output.txt'
vec = construct_sentence_vec(article)
'''
if(exist == 0):
construct_input_file(vec,input_name,art_title)
os.system('python takelab_sts/takelab_simple_features.py '+input_name+' > '+feature_name)
os.system('svm-predict '+feature_name+' model.txt ' + output_name)
os.system('python postprocess_scores.py '+ input_name+' ' + output_name)
matrix,title_sim = construct_similarity_matrix(output_name,len(vec))
'''
tmp = mail_struct()
tmp.name = title
tmp.text = vec
bot = re.compile(r'\_',re.S)
tmp.remove_stop_ver_title = stop_word_remove(bot.sub(' ',title))
matrix,title_sim = cal_similarity(tmp)
important = lexrank(matrix,len(vec),0.001)
for k in range(len(important)):
if(math.isnan(important[k])):
important[k] = 0
important = (important-min(important))/(max(important)-min(important))
title_sim = (title_sim-min(title_sim))/(max(title_sim)-min(title_sim))
sort_index = numpy.argsort(important)[::-1]
return vec,important,title_sim
