def umls_semantic_type_word( umls_string_cache , sentence ):
# Already cached?
if False and umls_string_cache.has_key( sentence ):
mapping = umls_string_cache.get_map( sentence )
else:
concepts = interface_umls.string_lookup( sentence )
concepts = [ singleton[0] for singleton in set(concepts) ]
umls_string_cache.add_map(sentence , concepts)
mapping = umls_string_cache.get_map(sentence)
return mapping
def span2concept(span):
rawstring = ' '.join(sentence[span[0]:span[1]+1])
# Already cached?
if cache.has_key( rawstring ):
return cache.get_map( rawstring )
else:
concept = interface_umls.string_lookup( rawstring )
if concept:
cache.add_map( rawstring , concept )
else:
cache.add_map( rawstring , [] )
return cache.get_map( rawstring )
def umls_semantic_context_of_words( umls_string_cache, sentence ):
#Defines the largest string span for the sentence.
WINDOW_SIZE = 7
# span of the umls concept of the largest substring
umls_context_list = []
# keys: tuple of (start,end) index of a substring
concept_span_dict = {}
# Each sublist functions as the mappings for each word.
for i in sentence:
umls_context_list.append( [] )
# finds the span for each substring of length 1 to currentWindowSize.
for currentWindowSize in range( 1 , WINDOW_SIZE ):
for ti in range( 0 , ( len(sentence) - currentWindowSize ) + 1 ):
rawstring = ""
for tj in range( ti , ti + currentWindowSize):
rawstring += ( sentence[tj] + " " )
#Each string is of length 1 to currentWindowSize.
rawstring = rawstring.strip()
# Not in cache yet?
if not( umls_string_cache.has_key( rawstring ) ):
# returns a tuple if there is a result or None is there is not.
concept = interface_umls.string_lookup( rawstring )
if not concept:
umls_string_cache.add_map( rawstring, None )
else:
umls_string_cache.add_map( rawstring, concept ) ;
#Store the concept into concept_span_dict with its span as a key.
concept_span_dict[(ti,ti+currentWindowSize-1)] = umls_string_cache.get_map( rawstring )
# For each substring if there is a span, then
# assign the concept to every word that is within in the substring
if umls_string_cache.get_map(rawstring):
for i in range( ti , ti + currentWindowSize ):
if len( umls_context_list[i] ) == 0:
umls_context_list[i].append([ti,ti+currentWindowSize-1])
else:
updated = 0
for j in umls_context_list[i]:
if j[0] >= ti and j[1] <= (ti+currentWindowSize-1):
j[0] = ti
j[1] = ( ti + currentWindowSize - 1 )
updated += 1
if not(updated):
val = [ti,ti+currentWindowSize-1]
if umls_context_list[i].count(val)== 0:
umls_context_list[i].append(val)
#create a list of sublists
# each sublist represents the contexts for which the word appears
mappings = []
for i in umls_context_list:
spans = i
if len(spans) == 0:
mappings.append( None )
else:
sub_mappings = []
for j in spans:
sub_mappings.append( concept_span_dict[tuple(j)])
# FIXME - Decided to concat rather than append (not sure why)
mappings += sub_mappings
return mappings