<s> from hazm import DadeganReader <EOL> from baaz import ChunkTreeInformationExtractor , DependencyTreeInformationExtractor <EOL> from hazm import Chunker , Normalizer , SequencePOSTagger , IOBTagger , DependencyParser , Lemmatizer <EOL> import codecs , re <EOL> from nltk import accuracy <EOL> from nltk . tree import Tree <EOL> dadegan = DadeganReader ( '<STR_LIT>' ) <EOL> tagger = SequencePOSTagger ( model = '<STR_LIT>' ) <EOL> lemmatizer = Lemmatizer ( ) <EOL> chunker = Chunker ( model = '<STR_LIT>' ) <EOL> parser = DependencyParser ( tagger , lemmatizer = lemmatizer ) <EOL> normalizer = Normalizer ( ) <EOL> chunk_extractor = ChunkTreeInformationExtractor ( ) <EOL> dep_extractor = DependencyTreeInformationExtractor ( ) <EOL> trees = list ( dadegan . chunked_trees ( ) ) <EOL> chunk_trees = trees [ : <NUM_LIT:100> ] + trees [ <NUM_LIT:200> : <NUM_LIT> ] <EOL> trees = list ( dadegan . trees ( ) ) <EOL> dep_trees = trees [ : <NUM_LIT:100> ] + trees [ <NUM_LIT:200> : <NUM_LIT> ] <EOL> indices = [ ] <EOL> def tag_sent ( chunks , args ) : <EOL> tagged_sent = [ ] <EOL> global_index = <NUM_LIT:0> <EOL> for chunk in chunks : <EOL> if type ( chunk ) is Tree : <EOL> tokens = chunk . leaves ( ) <EOL> else : <EOL> tokens = [ chunk ] <EOL> chunk_label = '<STR_LIT:O>' <EOL> for c in range ( global_index , global_index + len ( tokens ) ) : <EOL> info_label = '<STR_LIT:O>' <EOL> local_index = c - global_index <EOL> word = tokens [ local_index ] [ <NUM_LIT:0> ] <EOL> tag = tokens [ local_index ] [ <NUM_LIT:1> ] <EOL> if type ( chunk ) is Tree and local_index == <NUM_LIT:0> : <EOL> chunk_label = '<STR_LIT>' + chunk . label ( ) <EOL> elif type ( chunk ) is Tree : <EOL> chunk_label = '<STR_LIT>' + chunk . label ( ) <EOL> for arg1 in args [ <NUM_LIT:0> ] : <EOL> if c in arg1 : <EOL> if c == arg1 [ <NUM_LIT:0> ] : <EOL> info_label = '<STR_LIT>' <EOL> else : <EOL> info_label = '<STR_LIT>' <EOL> for arg2 in args [ <NUM_LIT:1> ] : <EOL> if info_label == '<STR_LIT:O>' and c in arg2 : <EOL> if c == arg2 [ <NUM_LIT:0> ] : <EOL> info_label = '<STR_LIT>' <EOL> else : <EOL> info_label = '<STR_LIT>' <EOL> for rel in args [ <NUM_LIT:2> ] : <EOL> if info_label == '<STR_LIT:O>' and c in rel : <EOL> if c == rel [ <NUM_LIT:0> ] : <EOL> info_label = '<STR_LIT>' <EOL> else : <EOL> info_label = '<STR_LIT>' <EOL> tagged_sent . append ( ( word , tag , chunk_label , info_label ) ) <EOL> global_index += len ( tokens ) <EOL> return tagged_sent <EOL> def positions ( info , sent ) : <EOL> info_list = [ ] <EOL> for arg in info : <EOL> arg_list = [ ] <EOL> index = sent . strip ( ) . find ( arg ) <EOL> if index >= <NUM_LIT:0> : <EOL> tokens = sent . split ( ) <EOL> for i in range ( len ( tokens ) ) : <EOL> index -= len ( tokens [ i ] ) + <NUM_LIT:1> <EOL> if index < <NUM_LIT:0> : <EOL> for c in range ( i , i + len ( arg . split ( ) ) ) : <EOL> arg_list . append ( c ) <EOL> break <EOL> info_list . append ( arg_list ) <EOL> return info_list <EOL> def gold_IOB_sents ( filename = '<STR_LIT>' ) : <EOL> """<STR_LIT>""" <EOL> lines = codecs . open ( filename , '<STR_LIT:r>' , encoding = '<STR_LIT:utf8>' ) . readlines ( ) <EOL> gold = [ ] <EOL> sentence = [ ] <EOL> for line in lines : <EOL> line = line . strip ( ) <EOL> if line == '<STR_LIT>' : <EOL> gold . append ( sentence ) <EOL> sentence = [ ] <EOL> else : <EOL> sentence . append ( tuple ( line . split ( '<STR_LIT:\t>' ) ) ) <EOL> return gold <EOL> def info2iob ( sentence , chunks , informations ) : <EOL> info_list = ( [ ] , [ ] , [ ] ) <EOL> for information in informations : <EOL> temp_list = positions ( information , sentence ) <EOL> for i in range ( <NUM_LIT:3> ) : <EOL> if temp_list [ i ] not in info_list [ i ] : <EOL> info_list [ i ] . append ( temp_list [ i ] ) <EOL> return tag_sent ( chunks , info_list ) <EOL> corrects = <NUM_LIT:0> <EOL> total = <NUM_LIT:0> <EOL> gold = gold_IOB_sents ( '<STR_LIT>' ) + gold_IOB_sents ( '<STR_LIT>' ) <EOL> sentences = [ ] <EOL> evaluation_sents = [ ] <EOL> for gold_sent in gold : <EOL> sentences . append ( [ w for w , t , c , l in gold_sent ] ) <EOL> dep_tagged_sents = [ ] <EOL> chunk_tagged_sents = [ ] <EOL> for number , gold_sent in enumerate ( gold ) : <EOL> sentence = '<STR_LIT:U+0020>' . join ( sentences [ number ] ) <EOL> chunk_tree = chunk_trees [ number ] <EOL> dep_tree = dep_trees [ number ] <EOL> chunk_informations = list ( chunk_extractor . extract ( chunk_tree ) ) <EOL> dep_informations = list ( dep_extractor . extract ( dep_tree ) ) <EOL> evaluation_sent = [ ( w , l ) for w , t , c , l in gold_sent ] <EOL> dep_tagged_sent = [ ( w , l ) for w , t , c , l in [ tokens for tokens in info2iob ( sentence , chunk_tree , dep_informations ) ] ] <EOL> chunk_tagged_sent = [ ( w , l ) for w , t , c , l in [ tokens for tokens in info2iob ( sentence , chunk_tree , chunk_informations ) ] ] <EOL> if len ( evaluation_sent ) == len ( dep_tagged_sent ) : <EOL> evaluation_sents . append ( evaluation_sent ) <EOL> dep_tagged_sents . append ( dep_tagged_sent ) <EOL> chunk_tagged_sents . append ( chunk_tagged_sent ) <EOL> else : <EOL> print ( chunk_tagged_sent ) <EOL> print ( ) <EOL> print ( '<STR_LIT>' % ( accuracy ( sum ( evaluation_sents , [ ] ) , sum ( dep_tagged_sents , [ ] ) ) ) ) <EOL> print ( '<STR_LIT>' % ( accuracy ( sum ( evaluation_sents , [ ] ) , sum ( chunk_tagged_sents , [ ] ) ) ) ) <EOL> information_tagger = IOBTagger ( model = '<STR_LIT>' ) <EOL> print ( information_tagger . evaluate ( gold ) ) </s>
