Source code for nltk.parse.transitionparser

# Natural Language Toolkit: Arc-Standard and Arc-eager Transition Based Parsers
#
# Author: Long Duong <longdt219@gmail.com>
#
# Copyright (C) 2001-2019 NLTK Project
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tempfile
import pickle

from os import remove
from copy import deepcopy
from operator import itemgetter

try:
    from numpy import array
    from scipy import sparse
    from sklearn.datasets import load_svmlight_file
    from sklearn import svm
except ImportError:
    pass

from nltk.parse import ParserI, DependencyGraph, DependencyEvaluator


[docs]class Configuration(object): """ Class for holding configuration which is the partial analysis of the input sentence. The transition based parser aims at finding set of operators that transfer the initial configuration to the terminal configuration. The configuration includes: - Stack: for storing partially proceeded words - Buffer: for storing remaining input words - Set of arcs: for storing partially built dependency tree This class also provides a method to represent a configuration as list of features. """ def __init__(self, dep_graph): """ :param dep_graph: the representation of an input in the form of dependency graph. :type dep_graph: DependencyGraph where the dependencies are not specified. """ # dep_graph.nodes contain list of token for a sentence self.stack = [0] # The root element self.buffer = list(range(1, len(dep_graph.nodes))) # The rest is in the buffer self.arcs = [] # empty set of arc self._tokens = dep_graph.nodes self._max_address = len(self.buffer) def __str__(self): return ( 'Stack : ' + str(self.stack) + ' Buffer : ' + str(self.buffer) + ' Arcs : ' + str(self.arcs) ) def _check_informative(self, feat, flag=False): """ Check whether a feature is informative The flag control whether "_" is informative or not """ if feat is None: return False if feat == '': return False if flag is False: if feat == '_': return False return True
[docs] def extract_features(self): """ Extract the set of features for the current configuration. Implement standard features as describe in Table 3.2 (page 31) in Dependency Parsing book by Sandra Kubler, Ryan McDonal, Joakim Nivre. Please note that these features are very basic. :return: list(str) """ result = [] # Todo : can come up with more complicated features set for better # performance. if len(self.stack) > 0: # Stack 0 stack_idx0 = self.stack[len(self.stack) - 1] token = self._tokens[stack_idx0] if self._check_informative(token['word'], True): result.append('STK_0_FORM_' + token['word']) if 'lemma' in token and self._check_informative(token['lemma']): result.append('STK_0_LEMMA_' + token['lemma']) if self._check_informative(token['tag']): result.append('STK_0_POS_' + token['tag']) if 'feats' in token and self._check_informative(token['feats']): feats = token['feats'].split("|") for feat in feats: result.append('STK_0_FEATS_' + feat) # Stack 1 if len(self.stack) > 1: stack_idx1 = self.stack[len(self.stack) - 2] token = self._tokens[stack_idx1] if self._check_informative(token['tag']): result.append('STK_1_POS_' + token['tag']) # Left most, right most dependency of stack[0] left_most = 1000000 right_most = -1 dep_left_most = '' dep_right_most = '' for (wi, r, wj) in self.arcs: if wi == stack_idx0: if (wj > wi) and (wj > right_most): right_most = wj dep_right_most = r if (wj < wi) and (wj < left_most): left_most = wj dep_left_most = r if self._check_informative(dep_left_most): result.append('STK_0_LDEP_' + dep_left_most) if self._check_informative(dep_right_most): result.append('STK_0_RDEP_' + dep_right_most) # Check Buffered 0 if len(self.buffer) > 0: # Buffer 0 buffer_idx0 = self.buffer[0] token = self._tokens[buffer_idx0] if self._check_informative(token['word'], True): result.append('BUF_0_FORM_' + token['word']) if 'lemma' in token and self._check_informative(token['lemma']): result.append('BUF_0_LEMMA_' + token['lemma']) if self._check_informative(token['tag']): result.append('BUF_0_POS_' + token['tag']) if 'feats' in token and self._check_informative(token['feats']): feats = token['feats'].split("|") for feat in feats: result.append('BUF_0_FEATS_' + feat) # Buffer 1 if len(self.buffer) > 1: buffer_idx1 = self.buffer[1] token = self._tokens[buffer_idx1] if self._check_informative(token['word'], True): result.append('BUF_1_FORM_' + token['word']) if self._check_informative(token['tag']): result.append('BUF_1_POS_' + token['tag']) if len(self.buffer) > 2: buffer_idx2 = self.buffer[2] token = self._tokens[buffer_idx2] if self._check_informative(token['tag']): result.append('BUF_2_POS_' + token['tag']) if len(self.buffer) > 3: buffer_idx3 = self.buffer[3] token = self._tokens[buffer_idx3] if self._check_informative(token['tag']): result.append('BUF_3_POS_' + token['tag']) # Left most, right most dependency of stack[0] left_most = 1000000 right_most = -1 dep_left_most = '' dep_right_most = '' for (wi, r, wj) in self.arcs: if wi == buffer_idx0: if (wj > wi) and (wj > right_most): right_most = wj dep_right_most = r if (wj < wi) and (wj < left_most): left_most = wj dep_left_most = r if self._check_informative(dep_left_most): result.append('BUF_0_LDEP_' + dep_left_most) if self._check_informative(dep_right_most): result.append('BUF_0_RDEP_' + dep_right_most) return result
[docs]class Transition(object): """ This class defines a set of transition which is applied to a configuration to get another configuration Note that for different parsing algorithm, the transition is different. """ # Define set of transitions LEFT_ARC = 'LEFTARC' RIGHT_ARC = 'RIGHTARC' SHIFT = 'SHIFT' REDUCE = 'REDUCE' def __init__(self, alg_option): """ :param alg_option: the algorithm option of this parser. Currently support `arc-standard` and `arc-eager` algorithm :type alg_option: str """ self._algo = alg_option if alg_option not in [ TransitionParser.ARC_STANDARD, TransitionParser.ARC_EAGER, ]: raise ValueError( " Currently we only support %s and %s " % (TransitionParser.ARC_STANDARD, TransitionParser.ARC_EAGER) )
[docs] def left_arc(self, conf, relation): """ Note that the algorithm for left-arc is quite similar except for precondition for both arc-standard and arc-eager :param configuration: is the current configuration :return : A new configuration or -1 if the pre-condition is not satisfied """ if (len(conf.buffer) <= 0) or (len(conf.stack) <= 0): return -1 if conf.buffer[0] == 0: # here is the Root element return -1 idx_wi = conf.stack[len(conf.stack) - 1] flag = True if self._algo == TransitionParser.ARC_EAGER: for (idx_parent, r, idx_child) in conf.arcs: if idx_child == idx_wi: flag = False if flag: conf.stack.pop() idx_wj = conf.buffer[0] conf.arcs.append((idx_wj, relation, idx_wi)) else: return -1
[docs] def right_arc(self, conf, relation): """ Note that the algorithm for right-arc is DIFFERENT for arc-standard and arc-eager :param configuration: is the current configuration :return : A new configuration or -1 if the pre-condition is not satisfied """ if (len(conf.buffer) <= 0) or (len(conf.stack) <= 0): return -1 if self._algo == TransitionParser.ARC_STANDARD: idx_wi = conf.stack.pop() idx_wj = conf.buffer[0] conf.buffer[0] = idx_wi conf.arcs.append((idx_wi, relation, idx_wj)) else: # arc-eager idx_wi = conf.stack[len(conf.stack) - 1] idx_wj = conf.buffer.pop(0) conf.stack.append(idx_wj) conf.arcs.append((idx_wi, relation, idx_wj))
[docs] def reduce(self, conf): """ Note that the algorithm for reduce is only available for arc-eager :param configuration: is the current configuration :return : A new configuration or -1 if the pre-condition is not satisfied """ if self._algo != TransitionParser.ARC_EAGER: return -1 if len(conf.stack) <= 0: return -1 idx_wi = conf.stack[len(conf.stack) - 1] flag = False for (idx_parent, r, idx_child) in conf.arcs: if idx_child == idx_wi: flag = True if flag: conf.stack.pop() # reduce it else: return -1
[docs] def shift(self, conf): """ Note that the algorithm for shift is the SAME for arc-standard and arc-eager :param configuration: is the current configuration :return : A new configuration or -1 if the pre-condition is not satisfied """ if len(conf.buffer) <= 0: return -1 idx_wi = conf.buffer.pop(0) conf.stack.append(idx_wi)
[docs]class TransitionParser(ParserI): """ Class for transition based parser. Implement 2 algorithms which are "arc-standard" and "arc-eager" """ ARC_STANDARD = 'arc-standard' ARC_EAGER = 'arc-eager' def __init__(self, algorithm): """ :param algorithm: the algorithm option of this parser. Currently support `arc-standard` and `arc-eager` algorithm :type algorithm: str """ if not (algorithm in [self.ARC_STANDARD, self.ARC_EAGER]): raise ValueError( " Currently we only support %s and %s " % (self.ARC_STANDARD, self.ARC_EAGER) ) self._algorithm = algorithm self._dictionary = {} self._transition = {} self._match_transition = {} def _get_dep_relation(self, idx_parent, idx_child, depgraph): p_node = depgraph.nodes[idx_parent] c_node = depgraph.nodes[idx_child] if c_node['word'] is None: return None # Root word if c_node['head'] == p_node['address']: return c_node['rel'] else: return None def _convert_to_binary_features(self, features): """ :param features: list of feature string which is needed to convert to binary features :type features: list(str) :return : string of binary features in libsvm format which is 'featureID:value' pairs """ unsorted_result = [] for feature in features: self._dictionary.setdefault(feature, len(self._dictionary)) unsorted_result.append(self._dictionary[feature]) # Default value of each feature is 1.0 return ' '.join( str(featureID) + ':1.0' for featureID in sorted(unsorted_result) ) def _is_projective(self, depgraph): arc_list = [] for key in depgraph.nodes: node = depgraph.nodes[key] if 'head' in node: childIdx = node['address'] parentIdx = node['head'] if parentIdx is not None: arc_list.append((parentIdx, childIdx)) for (parentIdx, childIdx) in arc_list: # Ensure that childIdx < parentIdx if childIdx > parentIdx: temp = childIdx childIdx = parentIdx parentIdx = temp for k in range(childIdx + 1, parentIdx): for m in range(len(depgraph.nodes)): if (m < childIdx) or (m > parentIdx): if (k, m) in arc_list: return False if (m, k) in arc_list: return False return True def _write_to_file(self, key, binary_features, input_file): """ write the binary features to input file and update the transition dictionary """ self._transition.setdefault(key, len(self._transition) + 1) self._match_transition[self._transition[key]] = key input_str = str(self._transition[key]) + ' ' + binary_features + '\n' input_file.write(input_str.encode('utf-8')) 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
[docs] 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)
[docs] 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
[docs]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 >>> import tempfile >>> import os >>> input_file = tempfile.NamedTemporaryFile(prefix='transition_parse.train', dir=tempfile.gettempdir(), delete=False) >>> parser_std = TransitionParser('arc-standard') >>> print(', '.join(parser_std._create_training_examples_arc_std([gold_sent], input_file))) Number of training examples : 1 Number of valid (projective) examples : 1 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 >>> parser_std.train([gold_sent],'temp.arcstd.model', verbose=False) Number of training examples : 1 Number of valid (projective) examples : 1 >>> remove(input_file.name) B. Check the ARC-EAGER training >>> input_file = tempfile.NamedTemporaryFile(prefix='transition_parse.train', dir=tempfile.gettempdir(),delete=False) >>> parser_eager = TransitionParser('arc-eager') >>> print(', '.join(parser_eager._create_training_examples_arc_eager([gold_sent], input_file))) Number of training examples : 1 Number of valid (projective) examples : 1 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 >>> parser_eager.train([gold_sent],'temp.arceager.model', verbose=False) Number of training examples : 1 Number of valid (projective) examples : 1 >>> remove(input_file.name) ###################### Check The Parsing Function ######################## A. Check the ARC-STANDARD parser >>> result = parser_std.parse([gold_sent], 'temp.arcstd.model') >>> de = DependencyEvaluator(result, [gold_sent]) >>> de.eval() >= (0, 0) True B. Check the ARC-EAGER parser >>> result = parser_eager.parse([gold_sent], 'temp.arceager.model') >>> de = DependencyEvaluator(result, [gold_sent]) >>> de.eval() >= (0, 0) True Remove test temporary files >>> remove('temp.arceager.model') >>> remove('temp.arcstd.model') Note that result is very poor because of only one training example. """