def apply_morfessor(target, source, env):
"""Applies a trained Morfessor model to an unseen word list.
Sources: morfessor model file, word list file
Targets: segmented word list
"""
parser = get_default_argparser()
args = parser.parse_args([])
io = MorfessorIO(encoding=args.encoding,
compound_separator=args.cseparator,
atom_separator=args.separator)
model = io.read_binary_model_file(source[0].rstr())
words = []
terms = {}
for fname in source[1:]:
try:
with meta_open(fname.rstr(), enc=None) as ifd:
for t in et.parse(ifd).getiterator("kw"):
text = list(t.getiterator("kwtext"))[0].text
words += text.strip().split()
except:
with meta_open(fname.rstr()) as ifd:
words = [l.strip().split()[0] for l in ifd]
words = set(sum([w.strip("-").split("-") for w in words if "_" not in w], []))
for w in words:
toks, score = model.viterbi_segment(w)
if len(toks) >= 2:
toks = ["%s+" % toks[0]] + ["+%s+" % t for t in toks[1:-1]] + ["+%s" % toks[-1]]
terms[w] = toks
with meta_open(target[0].rstr(), "w") as ofd:
ofd.write(("\n".join(sorted(["%s" % (" ".join(v)) for k, v in terms.iteritems()]))) + "\n")
return None
def train_morfessor(target, source, env):
"""Train a Morfessor model using a word list as input.
This builder is largely based on the code in the new Python version of Morfessor.
Note that it prevents splitting that would create a morph composed just of
non-acoustic graphemes.
Sources: word list file
Targets: segmented word list file, morfessor model file
"""
parser = get_default_argparser()
args = parser.parse_args([])
dampfunc = lambda x : x
if len(env.get("NON_ACOUSTIC_GRAPHEMES", [])) > 0:
rx_str = "((\\S(%(ALT)s))|(^(%(ALT)s)\\S))" % {"ALT" : "|".join([unichr(int(x, base=16)) for x in env.get("NON_ACOUSTIC_GRAPHEMES")])}
else:
rx_str = None
model = BaselineModel(forcesplit_list=env.get("FORCE_SPLIT", []),
corpusweight=1.0,
use_skips=False,
nosplit_re=rx_str)
io = MorfessorIO(encoding=args.encoding,
compound_separator=args.cseparator,
atom_separator=args.separator)
words = {}
with meta_open(source[0].rstr()) as ifd:
for line in ifd:
toks = line.strip().split()
for word in toks[0].split("-"):
if len(toks) == 1:
words[word] = 1
elif len(toks) == 2:
words[word] = words.get(word, 0) + int(toks[1])
else:
return "malformed vocabulary line: %s" % (line.strip())
words = {w : c for w, c in words.iteritems() if not re.match(env.get("NON_WORD_PATTERN", "^$"), w)}
model.load_data([(c, w, (w)) for w, c in words.iteritems()], args.freqthreshold, dampfunc, args.splitprob)
algparams = ()
develannots = None
e, c = model.train_batch(args.algorithm, algparams, develannots,
args.finish_threshold, args.maxepochs)
with meta_open(target[0].rstr(), "w") as ofd:
for n, morphs in model.get_segmentations():
if len(morphs) >= 2:
morphs = ["%s+" % morphs[0]] + ["+%s+" % t for t in morphs[1:-1]] + ["+%s" % morphs[-1]]
ofd.write(" ".join(morphs) + "\n")
io.write_binary_model_file(target[1].rstr(), model)
return None
