353 lines
11 KiB
Python
353 lines
11 KiB
Python
# Natural Language Toolkit: Chunk parsing API
|
|
#
|
|
# Copyright (C) 2001-2019 NLTK Project
|
|
# Author: Edward Loper <edloper@gmail.com>
|
|
# URL: <http://nltk.org/>
|
|
# For license information, see LICENSE.TXT
|
|
|
|
"""
|
|
Named entity chunker
|
|
"""
|
|
|
|
import os, re, pickle
|
|
from xml.etree import ElementTree as ET
|
|
|
|
from nltk.tag import ClassifierBasedTagger, pos_tag
|
|
|
|
try:
|
|
from nltk.classify import MaxentClassifier
|
|
except ImportError:
|
|
pass
|
|
|
|
from nltk.tree import Tree
|
|
from nltk.tokenize import word_tokenize
|
|
from nltk.data import find
|
|
|
|
from nltk.chunk.api import ChunkParserI
|
|
from nltk.chunk.util import ChunkScore
|
|
|
|
|
|
class NEChunkParserTagger(ClassifierBasedTagger):
|
|
"""
|
|
The IOB tagger used by the chunk parser.
|
|
"""
|
|
|
|
def __init__(self, train):
|
|
ClassifierBasedTagger.__init__(
|
|
self, train=train, classifier_builder=self._classifier_builder
|
|
)
|
|
|
|
def _classifier_builder(self, train):
|
|
return MaxentClassifier.train(
|
|
train, algorithm="megam", gaussian_prior_sigma=1, trace=2
|
|
)
|
|
|
|
def _english_wordlist(self):
|
|
try:
|
|
wl = self._en_wordlist
|
|
except AttributeError:
|
|
from nltk.corpus import words
|
|
|
|
self._en_wordlist = set(words.words("en-basic"))
|
|
wl = self._en_wordlist
|
|
return wl
|
|
|
|
def _feature_detector(self, tokens, index, history):
|
|
word = tokens[index][0]
|
|
pos = simplify_pos(tokens[index][1])
|
|
if index == 0:
|
|
prevword = prevprevword = None
|
|
prevpos = prevprevpos = None
|
|
prevshape = prevtag = prevprevtag = None
|
|
elif index == 1:
|
|
prevword = tokens[index - 1][0].lower()
|
|
prevprevword = None
|
|
prevpos = simplify_pos(tokens[index - 1][1])
|
|
prevprevpos = None
|
|
prevtag = history[index - 1][0]
|
|
prevshape = prevprevtag = None
|
|
else:
|
|
prevword = tokens[index - 1][0].lower()
|
|
prevprevword = tokens[index - 2][0].lower()
|
|
prevpos = simplify_pos(tokens[index - 1][1])
|
|
prevprevpos = simplify_pos(tokens[index - 2][1])
|
|
prevtag = history[index - 1]
|
|
prevprevtag = history[index - 2]
|
|
prevshape = shape(prevword)
|
|
if index == len(tokens) - 1:
|
|
nextword = nextnextword = None
|
|
nextpos = nextnextpos = None
|
|
elif index == len(tokens) - 2:
|
|
nextword = tokens[index + 1][0].lower()
|
|
nextpos = tokens[index + 1][1].lower()
|
|
nextnextword = None
|
|
nextnextpos = None
|
|
else:
|
|
nextword = tokens[index + 1][0].lower()
|
|
nextpos = tokens[index + 1][1].lower()
|
|
nextnextword = tokens[index + 2][0].lower()
|
|
nextnextpos = tokens[index + 2][1].lower()
|
|
|
|
# 89.6
|
|
features = {
|
|
"bias": True,
|
|
"shape": shape(word),
|
|
"wordlen": len(word),
|
|
"prefix3": word[:3].lower(),
|
|
"suffix3": word[-3:].lower(),
|
|
"pos": pos,
|
|
"word": word,
|
|
"en-wordlist": (word in self._english_wordlist()),
|
|
"prevtag": prevtag,
|
|
"prevpos": prevpos,
|
|
"nextpos": nextpos,
|
|
"prevword": prevword,
|
|
"nextword": nextword,
|
|
"word+nextpos": "{0}+{1}".format(word.lower(), nextpos),
|
|
"pos+prevtag": "{0}+{1}".format(pos, prevtag),
|
|
"shape+prevtag": "{0}+{1}".format(prevshape, prevtag),
|
|
}
|
|
|
|
return features
|
|
|
|
|
|
class NEChunkParser(ChunkParserI):
|
|
"""
|
|
Expected input: list of pos-tagged words
|
|
"""
|
|
|
|
def __init__(self, train):
|
|
self._train(train)
|
|
|
|
def parse(self, tokens):
|
|
"""
|
|
Each token should be a pos-tagged word
|
|
"""
|
|
tagged = self._tagger.tag(tokens)
|
|
tree = self._tagged_to_parse(tagged)
|
|
return tree
|
|
|
|
def _train(self, corpus):
|
|
# Convert to tagged sequence
|
|
corpus = [self._parse_to_tagged(s) for s in corpus]
|
|
|
|
self._tagger = NEChunkParserTagger(train=corpus)
|
|
|
|
def _tagged_to_parse(self, tagged_tokens):
|
|
"""
|
|
Convert a list of tagged tokens to a chunk-parse tree.
|
|
"""
|
|
sent = Tree("S", [])
|
|
|
|
for (tok, tag) in tagged_tokens:
|
|
if tag == "O":
|
|
sent.append(tok)
|
|
elif tag.startswith("B-"):
|
|
sent.append(Tree(tag[2:], [tok]))
|
|
elif tag.startswith("I-"):
|
|
if sent and isinstance(sent[-1], Tree) and sent[-1].label() == tag[2:]:
|
|
sent[-1].append(tok)
|
|
else:
|
|
sent.append(Tree(tag[2:], [tok]))
|
|
return sent
|
|
|
|
@staticmethod
|
|
def _parse_to_tagged(sent):
|
|
"""
|
|
Convert a chunk-parse tree to a list of tagged tokens.
|
|
"""
|
|
toks = []
|
|
for child in sent:
|
|
if isinstance(child, Tree):
|
|
if len(child) == 0:
|
|
print("Warning -- empty chunk in sentence")
|
|
continue
|
|
toks.append((child[0], "B-{0}".format(child.label())))
|
|
for tok in child[1:]:
|
|
toks.append((tok, "I-{0}".format(child.label())))
|
|
else:
|
|
toks.append((child, "O"))
|
|
return toks
|
|
|
|
|
|
def shape(word):
|
|
if re.match("[0-9]+(\.[0-9]*)?|[0-9]*\.[0-9]+$", word, re.UNICODE):
|
|
return "number"
|
|
elif re.match("\W+$", word, re.UNICODE):
|
|
return "punct"
|
|
elif re.match("\w+$", word, re.UNICODE):
|
|
if word.istitle():
|
|
return "upcase"
|
|
elif word.islower():
|
|
return "downcase"
|
|
else:
|
|
return "mixedcase"
|
|
else:
|
|
return "other"
|
|
|
|
|
|
def simplify_pos(s):
|
|
if s.startswith("V"):
|
|
return "V"
|
|
else:
|
|
return s.split("-")[0]
|
|
|
|
|
|
def postag_tree(tree):
|
|
# Part-of-speech tagging.
|
|
words = tree.leaves()
|
|
tag_iter = (pos for (word, pos) in pos_tag(words))
|
|
newtree = Tree("S", [])
|
|
for child in tree:
|
|
if isinstance(child, Tree):
|
|
newtree.append(Tree(child.label(), []))
|
|
for subchild in child:
|
|
newtree[-1].append((subchild, next(tag_iter)))
|
|
else:
|
|
newtree.append((child, next(tag_iter)))
|
|
return newtree
|
|
|
|
|
|
def load_ace_data(roots, fmt="binary", skip_bnews=True):
|
|
for root in roots:
|
|
for root, dirs, files in os.walk(root):
|
|
if root.endswith("bnews") and skip_bnews:
|
|
continue
|
|
for f in files:
|
|
if f.endswith(".sgm"):
|
|
for sent in load_ace_file(os.path.join(root, f), fmt):
|
|
yield sent
|
|
|
|
|
|
def load_ace_file(textfile, fmt):
|
|
print(" - {0}".format(os.path.split(textfile)[1]))
|
|
annfile = textfile + ".tmx.rdc.xml"
|
|
|
|
# Read the xml file, and get a list of entities
|
|
entities = []
|
|
with open(annfile, "r") as infile:
|
|
xml = ET.parse(infile).getroot()
|
|
for entity in xml.findall("document/entity"):
|
|
typ = entity.find("entity_type").text
|
|
for mention in entity.findall("entity_mention"):
|
|
if mention.get("TYPE") != "NAME":
|
|
continue # only NEs
|
|
s = int(mention.find("head/charseq/start").text)
|
|
e = int(mention.find("head/charseq/end").text) + 1
|
|
entities.append((s, e, typ))
|
|
|
|
# Read the text file, and mark the entities.
|
|
with open(textfile, "r") as infile:
|
|
text = infile.read()
|
|
|
|
# Strip XML tags, since they don't count towards the indices
|
|
text = re.sub("<(?!/?TEXT)[^>]+>", "", text)
|
|
|
|
# Blank out anything before/after <TEXT>
|
|
def subfunc(m):
|
|
return " " * (m.end() - m.start() - 6)
|
|
|
|
text = re.sub("[\s\S]*<TEXT>", subfunc, text)
|
|
text = re.sub("</TEXT>[\s\S]*", "", text)
|
|
|
|
# Simplify quotes
|
|
text = re.sub("``", ' "', text)
|
|
text = re.sub("''", '" ', text)
|
|
|
|
entity_types = set(typ for (s, e, typ) in entities)
|
|
|
|
# Binary distinction (NE or not NE)
|
|
if fmt == "binary":
|
|
i = 0
|
|
toks = Tree("S", [])
|
|
for (s, e, typ) in sorted(entities):
|
|
if s < i:
|
|
s = i # Overlapping! Deal with this better?
|
|
if e <= s:
|
|
continue
|
|
toks.extend(word_tokenize(text[i:s]))
|
|
toks.append(Tree("NE", text[s:e].split()))
|
|
i = e
|
|
toks.extend(word_tokenize(text[i:]))
|
|
yield toks
|
|
|
|
# Multiclass distinction (NE type)
|
|
elif fmt == "multiclass":
|
|
i = 0
|
|
toks = Tree("S", [])
|
|
for (s, e, typ) in sorted(entities):
|
|
if s < i:
|
|
s = i # Overlapping! Deal with this better?
|
|
if e <= s:
|
|
continue
|
|
toks.extend(word_tokenize(text[i:s]))
|
|
toks.append(Tree(typ, text[s:e].split()))
|
|
i = e
|
|
toks.extend(word_tokenize(text[i:]))
|
|
yield toks
|
|
|
|
else:
|
|
raise ValueError("bad fmt value")
|
|
|
|
|
|
# This probably belongs in a more general-purpose location (as does
|
|
# the parse_to_tagged function).
|
|
def cmp_chunks(correct, guessed):
|
|
correct = NEChunkParser._parse_to_tagged(correct)
|
|
guessed = NEChunkParser._parse_to_tagged(guessed)
|
|
ellipsis = False
|
|
for (w, ct), (w, gt) in zip(correct, guessed):
|
|
if ct == gt == "O":
|
|
if not ellipsis:
|
|
print(" {:15} {:15} {2}".format(ct, gt, w))
|
|
print(" {:15} {:15} {2}".format("...", "...", "..."))
|
|
ellipsis = True
|
|
else:
|
|
ellipsis = False
|
|
print(" {:15} {:15} {2}".format(ct, gt, w))
|
|
|
|
|
|
def build_model(fmt="binary"):
|
|
print("Loading training data...")
|
|
train_paths = [
|
|
find("corpora/ace_data/ace.dev"),
|
|
find("corpora/ace_data/ace.heldout"),
|
|
find("corpora/ace_data/bbn.dev"),
|
|
find("corpora/ace_data/muc.dev"),
|
|
]
|
|
train_trees = load_ace_data(train_paths, fmt)
|
|
train_data = [postag_tree(t) for t in train_trees]
|
|
print("Training...")
|
|
cp = NEChunkParser(train_data)
|
|
del train_data
|
|
|
|
print("Loading eval data...")
|
|
eval_paths = [find("corpora/ace_data/ace.eval")]
|
|
eval_trees = load_ace_data(eval_paths, fmt)
|
|
eval_data = [postag_tree(t) for t in eval_trees]
|
|
|
|
print("Evaluating...")
|
|
chunkscore = ChunkScore()
|
|
for i, correct in enumerate(eval_data):
|
|
guess = cp.parse(correct.leaves())
|
|
chunkscore.score(correct, guess)
|
|
if i < 3:
|
|
cmp_chunks(correct, guess)
|
|
print(chunkscore)
|
|
|
|
outfilename = "/tmp/ne_chunker_{0}.pickle".format(fmt)
|
|
print("Saving chunker to {0}...".format(outfilename))
|
|
|
|
with open(outfilename, "wb") as outfile:
|
|
pickle.dump(cp, outfile, -1)
|
|
|
|
return cp
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Make sure that the pickled object has the right class name:
|
|
from nltk.chunk.named_entity import build_model
|
|
|
|
build_model("binary")
|
|
build_model("multiclass")
|