775 lines
31 KiB
Python
775 lines
31 KiB
Python
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# Natural Language Toolkit: Arc-Standard and Arc-eager Transition Based Parsers
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#
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# Author: Long Duong <longdt219@gmail.com>
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#
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# Copyright (C) 2001-2018 NLTK Project
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tempfile
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import pickle
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from os import remove
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from copy import deepcopy
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from operator import itemgetter
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try:
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from numpy import array
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from scipy import sparse
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from sklearn.datasets import load_svmlight_file
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from sklearn import svm
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except ImportError:
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pass
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from nltk.parse import ParserI, DependencyGraph, DependencyEvaluator
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class Configuration(object):
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"""
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Class for holding configuration which is the partial analysis of the input sentence.
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The transition based parser aims at finding set of operators that transfer the initial
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configuration to the terminal configuration.
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The configuration includes:
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- Stack: for storing partially proceeded words
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- Buffer: for storing remaining input words
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- Set of arcs: for storing partially built dependency tree
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This class also provides a method to represent a configuration as list of features.
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"""
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def __init__(self, dep_graph):
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"""
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:param dep_graph: the representation of an input in the form of dependency graph.
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:type dep_graph: DependencyGraph where the dependencies are not specified.
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"""
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# dep_graph.nodes contain list of token for a sentence
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self.stack = [0] # The root element
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self.buffer = list(range(1, len(dep_graph.nodes))) # The rest is in the buffer
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self.arcs = [] # empty set of arc
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self._tokens = dep_graph.nodes
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self._max_address = len(self.buffer)
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def __str__(self):
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return 'Stack : ' + \
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str(self.stack) + ' Buffer : ' + str(self.buffer) + ' Arcs : ' + str(self.arcs)
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def _check_informative(self, feat, flag=False):
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"""
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Check whether a feature is informative
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The flag control whether "_" is informative or not
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"""
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if feat is None:
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return False
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if feat == '':
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return False
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if flag is False:
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if feat == '_':
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return False
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return True
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def extract_features(self):
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"""
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Extract the set of features for the current configuration. Implement standard features as describe in
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Table 3.2 (page 31) in Dependency Parsing book by Sandra Kubler, Ryan McDonal, Joakim Nivre.
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Please note that these features are very basic.
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:return: list(str)
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"""
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result = []
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# Todo : can come up with more complicated features set for better
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# performance.
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if len(self.stack) > 0:
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# Stack 0
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stack_idx0 = self.stack[len(self.stack) - 1]
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token = self._tokens[stack_idx0]
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if self._check_informative(token['word'], True):
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result.append('STK_0_FORM_' + token['word'])
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if 'lemma' in token and self._check_informative(token['lemma']):
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result.append('STK_0_LEMMA_' + token['lemma'])
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if self._check_informative(token['tag']):
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result.append('STK_0_POS_' + token['tag'])
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if 'feats' in token and self._check_informative(token['feats']):
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feats = token['feats'].split("|")
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for feat in feats:
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result.append('STK_0_FEATS_' + feat)
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# Stack 1
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if len(self.stack) > 1:
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stack_idx1 = self.stack[len(self.stack) - 2]
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token = self._tokens[stack_idx1]
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if self._check_informative(token['tag']):
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result.append('STK_1_POS_' + token['tag'])
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# Left most, right most dependency of stack[0]
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left_most = 1000000
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right_most = -1
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dep_left_most = ''
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dep_right_most = ''
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for (wi, r, wj) in self.arcs:
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if wi == stack_idx0:
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if (wj > wi) and (wj > right_most):
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right_most = wj
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dep_right_most = r
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if (wj < wi) and (wj < left_most):
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left_most = wj
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dep_left_most = r
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if self._check_informative(dep_left_most):
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result.append('STK_0_LDEP_' + dep_left_most)
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if self._check_informative(dep_right_most):
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result.append('STK_0_RDEP_' + dep_right_most)
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# Check Buffered 0
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if len(self.buffer) > 0:
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# Buffer 0
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buffer_idx0 = self.buffer[0]
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token = self._tokens[buffer_idx0]
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if self._check_informative(token['word'], True):
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result.append('BUF_0_FORM_' + token['word'])
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if 'lemma' in token and self._check_informative(token['lemma']):
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result.append('BUF_0_LEMMA_' + token['lemma'])
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if self._check_informative(token['tag']):
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result.append('BUF_0_POS_' + token['tag'])
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if 'feats' in token and self._check_informative(token['feats']):
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feats = token['feats'].split("|")
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for feat in feats:
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result.append('BUF_0_FEATS_' + feat)
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# Buffer 1
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if len(self.buffer) > 1:
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buffer_idx1 = self.buffer[1]
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token = self._tokens[buffer_idx1]
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if self._check_informative(token['word'], True):
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result.append('BUF_1_FORM_' + token['word'])
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if self._check_informative(token['tag']):
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result.append('BUF_1_POS_' + token['tag'])
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if len(self.buffer) > 2:
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buffer_idx2 = self.buffer[2]
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token = self._tokens[buffer_idx2]
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if self._check_informative(token['tag']):
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result.append('BUF_2_POS_' + token['tag'])
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if len(self.buffer) > 3:
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buffer_idx3 = self.buffer[3]
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token = self._tokens[buffer_idx3]
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if self._check_informative(token['tag']):
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result.append('BUF_3_POS_' + token['tag'])
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# Left most, right most dependency of stack[0]
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left_most = 1000000
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right_most = -1
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dep_left_most = ''
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dep_right_most = ''
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for (wi, r, wj) in self.arcs:
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if wi == buffer_idx0:
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if (wj > wi) and (wj > right_most):
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right_most = wj
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dep_right_most = r
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if (wj < wi) and (wj < left_most):
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left_most = wj
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dep_left_most = r
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if self._check_informative(dep_left_most):
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result.append('BUF_0_LDEP_' + dep_left_most)
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if self._check_informative(dep_right_most):
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result.append('BUF_0_RDEP_' + dep_right_most)
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return result
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class Transition(object):
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"""
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This class defines a set of transition which is applied to a configuration to get another configuration
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Note that for different parsing algorithm, the transition is different.
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"""
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# Define set of transitions
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LEFT_ARC = 'LEFTARC'
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RIGHT_ARC = 'RIGHTARC'
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SHIFT = 'SHIFT'
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REDUCE = 'REDUCE'
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def __init__(self, alg_option):
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"""
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:param alg_option: the algorithm option of this parser. Currently support `arc-standard` and `arc-eager` algorithm
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:type alg_option: str
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"""
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self._algo = alg_option
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if alg_option not in [
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TransitionParser.ARC_STANDARD,
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TransitionParser.ARC_EAGER]:
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raise ValueError(" Currently we only support %s and %s " %
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(TransitionParser.ARC_STANDARD, TransitionParser.ARC_EAGER))
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def left_arc(self, conf, relation):
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"""
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Note that the algorithm for left-arc is quite similar except for precondition for both arc-standard and arc-eager
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:param configuration: is the current configuration
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:return : A new configuration or -1 if the pre-condition is not satisfied
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"""
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if (len(conf.buffer) <= 0) or (len(conf.stack) <= 0):
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return -1
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if conf.buffer[0] == 0:
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# here is the Root element
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return -1
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idx_wi = conf.stack[len(conf.stack) - 1]
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flag = True
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if self._algo == TransitionParser.ARC_EAGER:
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for (idx_parent, r, idx_child) in conf.arcs:
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if idx_child == idx_wi:
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flag = False
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if flag:
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conf.stack.pop()
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idx_wj = conf.buffer[0]
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conf.arcs.append((idx_wj, relation, idx_wi))
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else:
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return -1
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def right_arc(self, conf, relation):
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"""
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Note that the algorithm for right-arc is DIFFERENT for arc-standard and arc-eager
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:param configuration: is the current configuration
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:return : A new configuration or -1 if the pre-condition is not satisfied
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"""
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if (len(conf.buffer) <= 0) or (len(conf.stack) <= 0):
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return -1
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if self._algo == TransitionParser.ARC_STANDARD:
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idx_wi = conf.stack.pop()
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idx_wj = conf.buffer[0]
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conf.buffer[0] = idx_wi
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conf.arcs.append((idx_wi, relation, idx_wj))
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else: # arc-eager
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idx_wi = conf.stack[len(conf.stack) - 1]
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idx_wj = conf.buffer.pop(0)
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conf.stack.append(idx_wj)
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conf.arcs.append((idx_wi, relation, idx_wj))
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def reduce(self, conf):
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"""
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Note that the algorithm for reduce is only available for arc-eager
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:param configuration: is the current configuration
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:return : A new configuration or -1 if the pre-condition is not satisfied
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"""
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if self._algo != TransitionParser.ARC_EAGER:
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return -1
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if len(conf.stack) <= 0:
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return -1
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idx_wi = conf.stack[len(conf.stack) - 1]
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flag = False
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for (idx_parent, r, idx_child) in conf.arcs:
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if idx_child == idx_wi:
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flag = True
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if flag:
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conf.stack.pop() # reduce it
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else:
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return -1
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def shift(self, conf):
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"""
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Note that the algorithm for shift is the SAME for arc-standard and arc-eager
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:param configuration: is the current configuration
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:return : A new configuration or -1 if the pre-condition is not satisfied
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"""
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if len(conf.buffer) <= 0:
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return -1
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idx_wi = conf.buffer.pop(0)
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conf.stack.append(idx_wi)
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class TransitionParser(ParserI):
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"""
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Class for transition based parser. Implement 2 algorithms which are "arc-standard" and "arc-eager"
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"""
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ARC_STANDARD = 'arc-standard'
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ARC_EAGER = 'arc-eager'
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def __init__(self, algorithm):
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"""
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:param algorithm: the algorithm option of this parser. Currently support `arc-standard` and `arc-eager` algorithm
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:type algorithm: str
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"""
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if not(algorithm in [self.ARC_STANDARD, self.ARC_EAGER]):
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raise ValueError(" Currently we only support %s and %s " %
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(self.ARC_STANDARD, self.ARC_EAGER))
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self._algorithm = algorithm
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self._dictionary = {}
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self._transition = {}
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self._match_transition = {}
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def _get_dep_relation(self, idx_parent, idx_child, depgraph):
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p_node = depgraph.nodes[idx_parent]
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c_node = depgraph.nodes[idx_child]
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if c_node['word'] is None:
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return None # Root word
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if c_node['head'] == p_node['address']:
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return c_node['rel']
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else:
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return None
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def _convert_to_binary_features(self, features):
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"""
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:param features: list of feature string which is needed to convert to binary features
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:type features: list(str)
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:return : string of binary features in libsvm format which is 'featureID:value' pairs
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"""
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unsorted_result = []
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for feature in features:
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self._dictionary.setdefault(feature, len(self._dictionary))
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unsorted_result.append(self._dictionary[feature])
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# Default value of each feature is 1.0
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return ' '.join(str(featureID) + ':1.0' for featureID in sorted(unsorted_result))
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def _is_projective(self, depgraph):
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arc_list = []
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for key in depgraph.nodes:
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node = depgraph.nodes[key]
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if 'head' in node:
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childIdx = node['address']
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parentIdx = node['head']
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if parentIdx is not None:
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arc_list.append((parentIdx, childIdx))
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for (parentIdx, childIdx) in arc_list:
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# Ensure that childIdx < parentIdx
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if childIdx > parentIdx:
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temp = childIdx
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childIdx = parentIdx
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parentIdx = temp
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for k in range(childIdx + 1, parentIdx):
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for m in range(len(depgraph.nodes)):
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if (m < childIdx) or (m > parentIdx):
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if (k, m) in arc_list:
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return False
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if (m, k) in arc_list:
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return False
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return True
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def _write_to_file(self, key, binary_features, input_file):
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"""
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write the binary features to input file and update the transition dictionary
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"""
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self._transition.setdefault(key, len(self._transition) + 1)
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self._match_transition[self._transition[key]] = key
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input_str = str(self._transition[key]) + ' ' + binary_features + '\n'
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input_file.write(input_str.encode('utf-8'))
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def _create_training_examples_arc_std(self, depgraphs, input_file):
|
||
|
|
"""
|
||
|
|
Create the training example in the libsvm format and write it to the input_file.
|
||
|
|
Reference : Page 32, Chapter 3. Dependency Parsing by Sandra Kubler, Ryan McDonal and Joakim Nivre (2009)
|
||
|
|
"""
|
||
|
|
operation = Transition(self.ARC_STANDARD)
|
||
|
|
count_proj = 0
|
||
|
|
training_seq = []
|
||
|
|
|
||
|
|
for depgraph in depgraphs:
|
||
|
|
if not self._is_projective(depgraph):
|
||
|
|
continue
|
||
|
|
|
||
|
|
count_proj += 1
|
||
|
|
conf = Configuration(depgraph)
|
||
|
|
while len(conf.buffer) > 0:
|
||
|
|
b0 = conf.buffer[0]
|
||
|
|
features = conf.extract_features()
|
||
|
|
binary_features = self._convert_to_binary_features(features)
|
||
|
|
|
||
|
|
if len(conf.stack) > 0:
|
||
|
|
s0 = conf.stack[len(conf.stack) - 1]
|
||
|
|
# Left-arc operation
|
||
|
|
rel = self._get_dep_relation(b0, s0, depgraph)
|
||
|
|
if rel is not None:
|
||
|
|
key = Transition.LEFT_ARC + ':' + rel
|
||
|
|
self._write_to_file(key, binary_features, input_file)
|
||
|
|
operation.left_arc(conf, rel)
|
||
|
|
training_seq.append(key)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Right-arc operation
|
||
|
|
rel = self._get_dep_relation(s0, b0, depgraph)
|
||
|
|
if rel is not None:
|
||
|
|
precondition = True
|
||
|
|
# Get the max-index of buffer
|
||
|
|
maxID = conf._max_address
|
||
|
|
|
||
|
|
for w in range(maxID + 1):
|
||
|
|
if w != b0:
|
||
|
|
relw = self._get_dep_relation(b0, w, depgraph)
|
||
|
|
if relw is not None:
|
||
|
|
if (b0, relw, w) not in conf.arcs:
|
||
|
|
precondition = False
|
||
|
|
|
||
|
|
if precondition:
|
||
|
|
key = Transition.RIGHT_ARC + ':' + rel
|
||
|
|
self._write_to_file(
|
||
|
|
key,
|
||
|
|
binary_features,
|
||
|
|
input_file)
|
||
|
|
operation.right_arc(conf, rel)
|
||
|
|
training_seq.append(key)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Shift operation as the default
|
||
|
|
key = Transition.SHIFT
|
||
|
|
self._write_to_file(key, binary_features, input_file)
|
||
|
|
operation.shift(conf)
|
||
|
|
training_seq.append(key)
|
||
|
|
|
||
|
|
print(" Number of training examples : " + str(len(depgraphs)))
|
||
|
|
print(" Number of valid (projective) examples : " + str(count_proj))
|
||
|
|
return training_seq
|
||
|
|
|
||
|
|
def _create_training_examples_arc_eager(self, depgraphs, input_file):
|
||
|
|
"""
|
||
|
|
Create the training example in the libsvm format and write it to the input_file.
|
||
|
|
Reference : 'A Dynamic Oracle for Arc-Eager Dependency Parsing' by Joav Goldberg and Joakim Nivre
|
||
|
|
"""
|
||
|
|
operation = Transition(self.ARC_EAGER)
|
||
|
|
countProj = 0
|
||
|
|
training_seq = []
|
||
|
|
|
||
|
|
for depgraph in depgraphs:
|
||
|
|
if not self._is_projective(depgraph):
|
||
|
|
continue
|
||
|
|
|
||
|
|
countProj += 1
|
||
|
|
conf = Configuration(depgraph)
|
||
|
|
while len(conf.buffer) > 0:
|
||
|
|
b0 = conf.buffer[0]
|
||
|
|
features = conf.extract_features()
|
||
|
|
binary_features = self._convert_to_binary_features(features)
|
||
|
|
|
||
|
|
if len(conf.stack) > 0:
|
||
|
|
s0 = conf.stack[len(conf.stack) - 1]
|
||
|
|
# Left-arc operation
|
||
|
|
rel = self._get_dep_relation(b0, s0, depgraph)
|
||
|
|
if rel is not None:
|
||
|
|
key = Transition.LEFT_ARC + ':' + rel
|
||
|
|
self._write_to_file(key, binary_features, input_file)
|
||
|
|
operation.left_arc(conf, rel)
|
||
|
|
training_seq.append(key)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Right-arc operation
|
||
|
|
rel = self._get_dep_relation(s0, b0, depgraph)
|
||
|
|
if rel is not None:
|
||
|
|
key = Transition.RIGHT_ARC + ':' + rel
|
||
|
|
self._write_to_file(key, binary_features, input_file)
|
||
|
|
operation.right_arc(conf, rel)
|
||
|
|
training_seq.append(key)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# reduce operation
|
||
|
|
flag = False
|
||
|
|
for k in range(s0):
|
||
|
|
if self._get_dep_relation(k, b0, depgraph) is not None:
|
||
|
|
flag = True
|
||
|
|
if self._get_dep_relation(b0, k, depgraph) is not None:
|
||
|
|
flag = True
|
||
|
|
if flag:
|
||
|
|
key = Transition.REDUCE
|
||
|
|
self._write_to_file(key, binary_features, input_file)
|
||
|
|
operation.reduce(conf)
|
||
|
|
training_seq.append(key)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Shift operation as the default
|
||
|
|
key = Transition.SHIFT
|
||
|
|
self._write_to_file(key, binary_features, input_file)
|
||
|
|
operation.shift(conf)
|
||
|
|
training_seq.append(key)
|
||
|
|
|
||
|
|
print(" Number of training examples : " + str(len(depgraphs)))
|
||
|
|
print(" Number of valid (projective) examples : " + str(countProj))
|
||
|
|
return training_seq
|
||
|
|
|
||
|
|
def train(self, depgraphs, modelfile, verbose=True):
|
||
|
|
"""
|
||
|
|
:param depgraphs : list of DependencyGraph as the training data
|
||
|
|
:type depgraphs : DependencyGraph
|
||
|
|
:param modelfile : file name to save the trained model
|
||
|
|
:type modelfile : str
|
||
|
|
"""
|
||
|
|
|
||
|
|
try:
|
||
|
|
input_file = tempfile.NamedTemporaryFile(
|
||
|
|
prefix='transition_parse.train',
|
||
|
|
dir=tempfile.gettempdir(),
|
||
|
|
delete=False)
|
||
|
|
|
||
|
|
if self._algorithm == self.ARC_STANDARD:
|
||
|
|
self._create_training_examples_arc_std(depgraphs, input_file)
|
||
|
|
else:
|
||
|
|
self._create_training_examples_arc_eager(depgraphs, input_file)
|
||
|
|
|
||
|
|
input_file.close()
|
||
|
|
# Using the temporary file to train the libsvm classifier
|
||
|
|
x_train, y_train = load_svmlight_file(input_file.name)
|
||
|
|
# The parameter is set according to the paper:
|
||
|
|
# Algorithms for Deterministic Incremental Dependency Parsing by Joakim Nivre
|
||
|
|
# Todo : because of probability = True => very slow due to
|
||
|
|
# cross-validation. Need to improve the speed here
|
||
|
|
model = svm.SVC(
|
||
|
|
kernel='poly',
|
||
|
|
degree=2,
|
||
|
|
coef0=0,
|
||
|
|
gamma=0.2,
|
||
|
|
C=0.5,
|
||
|
|
verbose=verbose,
|
||
|
|
probability=True)
|
||
|
|
|
||
|
|
model.fit(x_train, y_train)
|
||
|
|
# Save the model to file name (as pickle)
|
||
|
|
pickle.dump(model, open(modelfile, 'wb'))
|
||
|
|
finally:
|
||
|
|
remove(input_file.name)
|
||
|
|
|
||
|
|
def parse(self, depgraphs, modelFile):
|
||
|
|
"""
|
||
|
|
:param depgraphs: the list of test sentence, each sentence is represented as a dependency graph where the 'head' information is dummy
|
||
|
|
:type depgraphs: list(DependencyGraph)
|
||
|
|
:param modelfile: the model file
|
||
|
|
:type modelfile: str
|
||
|
|
:return: list (DependencyGraph) with the 'head' and 'rel' information
|
||
|
|
"""
|
||
|
|
result = []
|
||
|
|
# First load the model
|
||
|
|
model = pickle.load(open(modelFile, 'rb'))
|
||
|
|
operation = Transition(self._algorithm)
|
||
|
|
|
||
|
|
for depgraph in depgraphs:
|
||
|
|
conf = Configuration(depgraph)
|
||
|
|
while len(conf.buffer) > 0:
|
||
|
|
features = conf.extract_features()
|
||
|
|
col = []
|
||
|
|
row = []
|
||
|
|
data = []
|
||
|
|
for feature in features:
|
||
|
|
if feature in self._dictionary:
|
||
|
|
col.append(self._dictionary[feature])
|
||
|
|
row.append(0)
|
||
|
|
data.append(1.0)
|
||
|
|
np_col = array(sorted(col)) # NB : index must be sorted
|
||
|
|
np_row = array(row)
|
||
|
|
np_data = array(data)
|
||
|
|
|
||
|
|
x_test = sparse.csr_matrix((np_data, (np_row, np_col)), shape=(1, len(self._dictionary)))
|
||
|
|
|
||
|
|
# It's best to use decision function as follow BUT it's not supported yet for sparse SVM
|
||
|
|
# Using decision funcion to build the votes array
|
||
|
|
#dec_func = model.decision_function(x_test)[0]
|
||
|
|
#votes = {}
|
||
|
|
#k = 0
|
||
|
|
# for i in range(len(model.classes_)):
|
||
|
|
# for j in range(i+1, len(model.classes_)):
|
||
|
|
# #if dec_func[k] > 0:
|
||
|
|
# votes.setdefault(i,0)
|
||
|
|
# votes[i] +=1
|
||
|
|
# else:
|
||
|
|
# votes.setdefault(j,0)
|
||
|
|
# votes[j] +=1
|
||
|
|
# k +=1
|
||
|
|
# Sort votes according to the values
|
||
|
|
#sorted_votes = sorted(votes.items(), key=itemgetter(1), reverse=True)
|
||
|
|
|
||
|
|
# We will use predict_proba instead of decision_function
|
||
|
|
prob_dict = {}
|
||
|
|
pred_prob = model.predict_proba(x_test)[0]
|
||
|
|
for i in range(len(pred_prob)):
|
||
|
|
prob_dict[i] = pred_prob[i]
|
||
|
|
sorted_Prob = sorted(
|
||
|
|
prob_dict.items(),
|
||
|
|
key=itemgetter(1),
|
||
|
|
reverse=True)
|
||
|
|
|
||
|
|
# Note that SHIFT is always a valid operation
|
||
|
|
for (y_pred_idx, confidence) in sorted_Prob:
|
||
|
|
#y_pred = model.predict(x_test)[0]
|
||
|
|
# From the prediction match to the operation
|
||
|
|
y_pred = model.classes_[y_pred_idx]
|
||
|
|
|
||
|
|
if y_pred in self._match_transition:
|
||
|
|
strTransition = self._match_transition[y_pred]
|
||
|
|
baseTransition = strTransition.split(":")[0]
|
||
|
|
|
||
|
|
if baseTransition == Transition.LEFT_ARC:
|
||
|
|
if operation.left_arc(conf, strTransition.split(":")[1]) != -1:
|
||
|
|
break
|
||
|
|
elif baseTransition == Transition.RIGHT_ARC:
|
||
|
|
if operation.right_arc(conf, strTransition.split(":")[1]) != -1:
|
||
|
|
break
|
||
|
|
elif baseTransition == Transition.REDUCE:
|
||
|
|
if operation.reduce(conf) != -1:
|
||
|
|
break
|
||
|
|
elif baseTransition == Transition.SHIFT:
|
||
|
|
if operation.shift(conf) != -1:
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
raise ValueError("The predicted transition is not recognized, expected errors")
|
||
|
|
|
||
|
|
# Finish with operations build the dependency graph from Conf.arcs
|
||
|
|
|
||
|
|
new_depgraph = deepcopy(depgraph)
|
||
|
|
for key in new_depgraph.nodes:
|
||
|
|
node = new_depgraph.nodes[key]
|
||
|
|
node['rel'] = ''
|
||
|
|
# With the default, all the token depend on the Root
|
||
|
|
node['head'] = 0
|
||
|
|
for (head, rel, child) in conf.arcs:
|
||
|
|
c_node = new_depgraph.nodes[child]
|
||
|
|
c_node['head'] = head
|
||
|
|
c_node['rel'] = rel
|
||
|
|
result.append(new_depgraph)
|
||
|
|
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
def demo():
|
||
|
|
"""
|
||
|
|
>>> from nltk.parse import DependencyGraph, DependencyEvaluator
|
||
|
|
>>> from nltk.parse.transitionparser import TransitionParser, Configuration, Transition
|
||
|
|
>>> gold_sent = DependencyGraph(\"""
|
||
|
|
... Economic JJ 2 ATT
|
||
|
|
... news NN 3 SBJ
|
||
|
|
... has VBD 0 ROOT
|
||
|
|
... little JJ 5 ATT
|
||
|
|
... effect NN 3 OBJ
|
||
|
|
... on IN 5 ATT
|
||
|
|
... financial JJ 8 ATT
|
||
|
|
... markets NNS 6 PC
|
||
|
|
... . . 3 PU
|
||
|
|
... \""")
|
||
|
|
|
||
|
|
>>> conf = Configuration(gold_sent)
|
||
|
|
|
||
|
|
###################### Check the Initial Feature ########################
|
||
|
|
|
||
|
|
>>> print(', '.join(conf.extract_features()))
|
||
|
|
STK_0_POS_TOP, BUF_0_FORM_Economic, BUF_0_LEMMA_Economic, BUF_0_POS_JJ, BUF_1_FORM_news, BUF_1_POS_NN, BUF_2_POS_VBD, BUF_3_POS_JJ
|
||
|
|
|
||
|
|
###################### Check The Transition #######################
|
||
|
|
Check the Initialized Configuration
|
||
|
|
>>> print(conf)
|
||
|
|
Stack : [0] Buffer : [1, 2, 3, 4, 5, 6, 7, 8, 9] Arcs : []
|
||
|
|
|
||
|
|
A. Do some transition checks for ARC-STANDARD
|
||
|
|
|
||
|
|
>>> operation = Transition('arc-standard')
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf, "ATT")
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf,"SBJ")
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf, "ATT")
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf, "ATT")
|
||
|
|
|
||
|
|
Middle Configuration and Features Check
|
||
|
|
>>> print(conf)
|
||
|
|
Stack : [0, 3, 5, 6] Buffer : [8, 9] Arcs : [(2, 'ATT', 1), (3, 'SBJ', 2), (5, 'ATT', 4), (8, 'ATT', 7)]
|
||
|
|
|
||
|
|
>>> print(', '.join(conf.extract_features()))
|
||
|
|
STK_0_FORM_on, STK_0_LEMMA_on, STK_0_POS_IN, STK_1_POS_NN, BUF_0_FORM_markets, BUF_0_LEMMA_markets, BUF_0_POS_NNS, BUF_1_FORM_., BUF_1_POS_., BUF_0_LDEP_ATT
|
||
|
|
|
||
|
|
>>> operation.right_arc(conf, "PC")
|
||
|
|
>>> operation.right_arc(conf, "ATT")
|
||
|
|
>>> operation.right_arc(conf, "OBJ")
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.right_arc(conf, "PU")
|
||
|
|
>>> operation.right_arc(conf, "ROOT")
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
|
||
|
|
Terminated Configuration Check
|
||
|
|
>>> print(conf)
|
||
|
|
Stack : [0] Buffer : [] Arcs : [(2, 'ATT', 1), (3, 'SBJ', 2), (5, 'ATT', 4), (8, 'ATT', 7), (6, 'PC', 8), (5, 'ATT', 6), (3, 'OBJ', 5), (3, 'PU', 9), (0, 'ROOT', 3)]
|
||
|
|
|
||
|
|
|
||
|
|
B. Do some transition checks for ARC-EAGER
|
||
|
|
|
||
|
|
>>> conf = Configuration(gold_sent)
|
||
|
|
>>> operation = Transition('arc-eager')
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf,'ATT')
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf,'SBJ')
|
||
|
|
>>> operation.right_arc(conf,'ROOT')
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf,'ATT')
|
||
|
|
>>> operation.right_arc(conf,'OBJ')
|
||
|
|
>>> operation.right_arc(conf,'ATT')
|
||
|
|
>>> operation.shift(conf)
|
||
|
|
>>> operation.left_arc(conf,'ATT')
|
||
|
|
>>> operation.right_arc(conf,'PC')
|
||
|
|
>>> operation.reduce(conf)
|
||
|
|
>>> operation.reduce(conf)
|
||
|
|
>>> operation.reduce(conf)
|
||
|
|
>>> operation.right_arc(conf,'PU')
|
||
|
|
>>> print(conf)
|
||
|
|
Stack : [0, 3, 9] Buffer : [] Arcs : [(2, 'ATT', 1), (3, 'SBJ', 2), (0, 'ROOT', 3), (5, 'ATT', 4), (3, 'OBJ', 5), (5, 'ATT', 6), (8, 'ATT', 7), (6, 'PC', 8), (3, 'PU', 9)]
|
||
|
|
|
||
|
|
###################### Check The Training Function #######################
|
||
|
|
|
||
|
|
A. Check the ARC-STANDARD training
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>>> import tempfile
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>>> import os
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>>> input_file = tempfile.NamedTemporaryFile(prefix='transition_parse.train', dir=tempfile.gettempdir(), delete=False)
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>>> parser_std = TransitionParser('arc-standard')
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>>> print(', '.join(parser_std._create_training_examples_arc_std([gold_sent], input_file)))
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Number of training examples : 1
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Number of valid (projective) examples : 1
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SHIFT, LEFTARC:ATT, SHIFT, LEFTARC:SBJ, SHIFT, SHIFT, LEFTARC:ATT, SHIFT, SHIFT, SHIFT, LEFTARC:ATT, RIGHTARC:PC, RIGHTARC:ATT, RIGHTARC:OBJ, SHIFT, RIGHTARC:PU, RIGHTARC:ROOT, SHIFT
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>>> parser_std.train([gold_sent],'temp.arcstd.model', verbose=False)
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Number of training examples : 1
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Number of valid (projective) examples : 1
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>>> remove(input_file.name)
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B. Check the ARC-EAGER training
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>>> input_file = tempfile.NamedTemporaryFile(prefix='transition_parse.train', dir=tempfile.gettempdir(),delete=False)
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>>> parser_eager = TransitionParser('arc-eager')
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>>> print(', '.join(parser_eager._create_training_examples_arc_eager([gold_sent], input_file)))
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Number of training examples : 1
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Number of valid (projective) examples : 1
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SHIFT, LEFTARC:ATT, SHIFT, LEFTARC:SBJ, RIGHTARC:ROOT, SHIFT, LEFTARC:ATT, RIGHTARC:OBJ, RIGHTARC:ATT, SHIFT, LEFTARC:ATT, RIGHTARC:PC, REDUCE, REDUCE, REDUCE, RIGHTARC:PU
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>>> parser_eager.train([gold_sent],'temp.arceager.model', verbose=False)
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Number of training examples : 1
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Number of valid (projective) examples : 1
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>>> remove(input_file.name)
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###################### Check The Parsing Function ########################
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A. Check the ARC-STANDARD parser
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>>> result = parser_std.parse([gold_sent], 'temp.arcstd.model')
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>>> de = DependencyEvaluator(result, [gold_sent])
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>>> de.eval() >= (0, 0)
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True
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B. Check the ARC-EAGER parser
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>>> result = parser_eager.parse([gold_sent], 'temp.arceager.model')
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>>> de = DependencyEvaluator(result, [gold_sent])
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>>> de.eval() >= (0, 0)
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True
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Remove test temporary files
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>>> remove('temp.arceager.model')
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>>> remove('temp.arcstd.model')
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||
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|
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Note that result is very poor because of only one training example.
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"""
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