from itertools import chain import os import cdec.configobj from cdec.sa.features import EgivenFCoherent, SampleCountF, CountEF,\ MaxLexEgivenF, MaxLexFgivenE, IsSingletonF, IsSingletonFE import cdec.sa # maximum span of a grammar rule in TEST DATA MAX_INITIAL_SIZE = 15 class GrammarExtractor: def __init__(self, config): if isinstance(config, str) or isinstance(config, unicode): if not os.path.exists(config): raise IOError('cannot read configuration from {0}'.format(config)) config = cdec.configobj.ConfigObj(config, unrepr=True) alignment = cdec.sa.Alignment(from_binary=config['a_file']) self.factory = cdec.sa.HieroCachingRuleFactory( # compiled alignment object (REQUIRED) alignment, # name of generic nonterminal used by Hiero category="[X]", # maximum number of contiguous chunks of terminal symbols in RHS of a rule max_chunks=config['max_nt']+1, # maximum span of a grammar rule in TEST DATA max_initial_size=MAX_INITIAL_SIZE, # maximum number of symbols (both T and NT) allowed in a rule max_length=config['max_len'], # maximum number of nonterminals allowed in a rule (set >2 at your own risk) max_nonterminals=config['max_nt'], # maximum number of contiguous chunks of terminal symbols # in target-side RHS of a rule. max_target_chunks=config['max_nt']+1, # maximum number of target side symbols (both T and NT) allowed in a rule. max_target_length=MAX_INITIAL_SIZE, # minimum span of a nonterminal in the RHS of a rule in TEST DATA min_gap_size=1, # filename of file containing precomputed collocations precompute_file=config['precompute_file'], # maximum frequency rank of patterns used to compute triples (< 20) precompute_secondary_rank=config['rank2'], # maximum frequency rank of patterns used to compute collocations (< 300) precompute_rank=config['rank1'], # require extracted rules to have at least one aligned word require_aligned_terminal=True, # require each contiguous chunk of extracted rules # to have at least one aligned word require_aligned_chunks=False, # maximum span of a grammar rule extracted from TRAINING DATA train_max_initial_size=config['max_size'], # minimum span of an RHS nonterminal in a rule extracted from TRAINING DATA train_min_gap_size=config['min_gap'], # True if phrases should be tight, False otherwise (better but slower) tight_phrases=True, ) # lexical weighting tables tt = cdec.sa.BiLex(from_binary=config['lex_file']) scorer = cdec.sa.Scorer(EgivenFCoherent, SampleCountF, CountEF, MaxLexFgivenE(tt), MaxLexEgivenF(tt), IsSingletonF, IsSingletonFE) fsarray = cdec.sa.SuffixArray(from_binary=config['f_sa_file']) edarray = cdec.sa.DataArray(from_binary=config['e_file']) # lower=faster, higher=better; improvements level off above 200-300 range, # -1 = don't sample, use all data (VERY SLOW!) sampler = cdec.sa.Sampler(300, fsarray) self.factory.configure(fsarray, edarray, sampler, scorer) def grammar(self, sentence): if isinstance(sentence, unicode): sentence = sentence.encode('utf8') cnet = chain(('',), sentence.split(), ('',)) cnet = (cdec.sa.sym_fromstring(word, terminal=True) for word in cnet) cnet = tuple(((word, None, 1), ) for word in cnet) return self.factory.input(cnet)