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PerceptronTagger.py
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# -*- coding: utf-8 -*-
# Author: XuMing <[email protected]>
# Data: 17/8/10
# Brief: 平均感知机：词性标注测试
import os
import random
from collections import defaultdict
import pickle
import logging
from AveragePerceptron import AveragePerceptron
PICKLE = "../data/bp/trontagger-0.1.pkg"
TRAIN_FILE_PATH = "../data/bp/train.txt"
TEST_FILE_PATH = "../data/bp/test.txt"
class PerceptronTagger():
START = ['-START-', '-START2-']
END = ['-END-', '-END2-']
AP_MODEL_LOC = os.path.join(os.path.dirname(__file__), PICKLE)
def __init__(self, load=True):
self.model = AveragePerceptron()
self.tagdict = {}
self.classes = set()
if load:
self.load(self.AP_MODEL_LOC)
def tag(self, corpus):
s_split = lambda t: t.split('\n')
w_split = lambda s: s.split()
def split_sents(corpus):
for s in s_split(corpus):
yield w_split(s)
prev, prev2 = self.START
tokens = []
for words in split_sents(corpus):
context = self.START + [self._normalize(w) for w in words] + self.END
for i, word in enumerate(words):
tag = self.tagdict.get(word)
if not tag:
features = self._get_features(i, word, context, prev, prev2)
tag = self.model.predict(features)
tokens.append((word, tag))
prev2 = prev
prev = tag
return tokens
def load(self, loc):
try:
w_td_c = pickle.load(open(loc, 'rb'))
except IOError:
raise IOError("Missing trontagger.pkg file.")
self.model.weights, self.tagdict, self.classes = w_td_c
self.model.classes = self.classes
return None
def _normalize(self, word):
if '-' in word and word[0] != '-':
return '!HYPHEN'
elif word.isdigit() and len(word) == 4:
return '!YEAR'
elif word[0].isdigit():
return '!DIGITS'
else:
return word.lower()
def _get_features(self, i, word, context, prev, prev2):
i += len(self.START)
features = defaultdict(int)
def add(name, *args):
features[' '.join((name,) + tuple(args))] += 1
# constant feature
add('bias')
add('i suffix', word[-3:])
add('i pref1', word[0])
add('i-1 tag', prev)
add('i-2 tag', prev2)
add('i tag+i-2 tag', prev, prev2)
add('i word', context[i])
add('i-1 tag+i word', prev, context[i])
add('i-1 word', context[i - 1])
add('i-1 suffix', context[i - 1][-3:])
add('i-2 word', context[i - 2])
add('i+1 word', context[i + 1])
add('i+1 suffix', context[i + 1][-3:])
add('i+2 word', context[i + 2])
return features
def _make_tagdict(self, sentences):
counts = defaultdict(lambda: defaultdict(int))
for words, tags in sentences:
for word, tag in zip(words, tags):
counts[word][tag] += 1
self.classes.add(tag)
freq_thresh = 20
ambiguity_thresh = 0.97
for word, tag_freqs in counts.items():
tag, mode = max(tag_freqs.items(), key=lambda item: item[1])
n = sum(tag_freqs.values())
if n >= freq_thresh and (float(mode) / n) >= ambiguity_thresh:
self.tagdict[word] = tag
def _pc(self, n, d):
return (float(n) / d) * 100
def train(self, sentences, save_loc=None, nr_iter=5):
self._make_tagdict(sentences)
self.model.classes = self.classes
for iter_ in range(nr_iter):
c = 0
n = 0
for words, tags in sentences:
prev, prev2 = self.START
context = self.START + [self._normalize(w) for w in words] + self.END
for i, word in enumerate(words):
guess = self.tagdict.get(word)
if not guess:
feats = self._get_features(i, word, context, prev, prev2)
guess = self.model.predict(feats)
self.model.update(tags[i], guess, feats)
prev2 = prev
prev = guess
c += guess == tags[i]
n += 1
random.shuffle(sentences)
logging.info("Iter {0}: {1}/{2}={3}".format(iter_, c, n, self._pc(c, n)))
self.model.average_weights()
if save_loc is not None:
pickle.dump((self.model.weights, self.tagdict, self.classes),
open(save_loc, 'wb'), -1)
return None
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
tagger = PerceptronTagger(False)
try:
tagger.load(PICKLE)
print(tagger.tag("how are you ?"))
logging.info("Start testing...")
right = 0.0
total = 0.0
sentence = ([], [])
for line in open(TEST_FILE_PATH):
params = line.split()
if len(params) != 2: continue
sentence[0].append(params[0])
sentence[1].append(params[1])
if params[0] == ".":
text = ""
words = sentence[0]
tags = sentence[1]
for i, word in enumerate(words):
text += word
if i < len(words):
text += " "
outputs = tagger.tag(text)
assert len(tags) == len(outputs)
total += len(tags)
for o, t in zip(outputs, tags):
if o[1].strip() == t:
right += 1
sentence = ([], [])
logging.info("Precision : %f", right / total)
except IOError:
logging.info("Reading corpus...")
training_data = []
sentence = ([], [])
for line in open(TRAIN_FILE_PATH):
params = line.split('\t')
sentence[0].append(params[0])
sentence[1].append(params[1])
if params[0] == ".":
training_data.append(sentence)
sentence = ([], [])
logging.info("training corpus size: %d", len(training_data))
logging.info("Start training...")
tagger.train(training_data, save_loc=PICKLE)
logging.info("training end.")