def _extract_concepts(document,terminology,MaxMatcher):
"""
document:
db:
terminology:
MaxMatcher:
returns:
Concept List
"""
# Set threshold
op = Operation()
threshold = 0.95
doc_token = document
#print "len(doc_token) " , len(doc_token)
candidate_concepts = []
#Prepare a dictionary for MaxMatcher result of tokens.
for token_row in doc_token:
if token_row not in MaxMatcher.keys():
extracted_concepts = DB._execute("select cid, sig from "+ terminology +"_mm where word = '" + token_row + "'")
MaxMatcher[token_row] = extracted_concepts
for current_token_counter in range(len(doc_token)-3): #skip the last 3 token
current_token = doc_token[current_token_counter]
skip_counter = 0 # Number of skips
skip_limit = 2 #Skip limit
extracted_concepts = MaxMatcher[current_token]
current_token_concepts = Set()
current_token_score = dict()
for c in extracted_concepts: # Create T_c
current_token_concepts.add(c[0])
current_token_score[c[0]] = c[1]
next_token_counter = 1 # Next word counter
next_token = doc_token[ current_token_counter + next_token_counter ] # t is the next word
while (skip_counter < skip_limit):
extracted_concepts = MaxMatcher[next_token]
next_token_concepts = Set()
next_token_score = dict()
for c in extracted_concepts:
next_token_concepts.add(c[0])
next_token_score[c[0]] = c[1]
mutual_concepts = next_token_concepts & current_token_concepts
if len(mutual_concepts) == 0:
skip_counter = skip_counter + 1
else:
current_token_concepts = mutual_concepts
for c in current_token_concepts:
current_token_score[c] += next_token_score[c]
next_token_counter += 1
if (current_token_counter + next_token_counter) < len (doc_token):
next_token = doc_token[ current_token_counter + next_token_counter ]
else:
break
candidate_concepts = op.union( candidate_concepts , [c for c in current_token_concepts if current_token_score[c]>threshold])
#print "-----------------------------------------------"
#print document
#print candidate_concepts
#print "-----------------------------------------------"
return candidate_concepts
def Indexing(self):
'''
IR Indexing Operations
- Elimination of Stopwords
-
'''
DB._execute("DELETE from collection_index")
print "Indexing is started..."
tp = TextProcessor()
Collection._load()
Collection._load_tags() #loading document with PMID, tags and abstracts
for doc in Collection._documents:
index_list = []
for term in doc.abstract:
index_list.append(term)
if GlobalVariables.global_context_activated:
for term in doc.tag:
index_list.append(term)
index_list = tp.EliminateStopWords(index_list)
index_list = tp.Stem(index_list)
doc.set_index(index_list)
print "Indexing is Done!"
