Source code for vnlp.dependency_parser.treestack_dp

from typing import List, Tuple

import pickle

import tensorflow as tf
import numpy as np

from ..stemmer_morph_analyzer import StemmerAnalyzer
from ..part_of_speech_tagger import PoSTagger
from ..tokenizer import TreebankWordTokenize
from ..utils import check_and_download, load_keras_tokenizer
from .utils import dp_pos_to_displacy_format, decode_arc_label_vector
from ._treestack_utils import (
    create_dependency_parser_model,
    process_single_word_input,
)

# Resolving parent dependencies
from inspect import getsourcefile
import os
import sys

current_path = os.path.abspath(getsourcefile(lambda: 0))
current_dir = os.path.dirname(current_path)
parent_dir = current_dir[: current_dir.rfind(os.path.sep)]
sys.path.insert(0, parent_dir)


RESOURCES_PATH = os.path.join(os.path.dirname(__file__), "resources/")

PROD_WEIGHTS_LOC = RESOURCES_PATH + "DP_TreeStack_prod.weights"
EVAL_WEIGHTS_LOC = RESOURCES_PATH + "DP_TreeStack_eval.weights"
WORD_EMBEDDING_MATRIX_LOC = os.path.abspath(
    os.path.join(
        os.path.dirname(__file__),
        "..",
        "resources/TBWTokenized_word_embedding.matrix",
    )
)

PROD_WEIGHTS_LINK = "https://vnlp-model-weights.s3.eu-west-1.amazonaws.com/DP_TreeStack_prod.weights"
EVAL_WEIGHTS_LINK = "https://vnlp-model-weights.s3.eu-west-1.amazonaws.com/DP_TreeStack_eval.weights"
WORD_EMBEDDING_MATRIX_LINK = "https://vnlp-model-weights.s3.eu-west-1.amazonaws.com/TBWTokenized_word_embedding.matrix"

TOKENIZER_WORD_LOC = os.path.abspath(
    os.path.join(
        os.path.dirname(__file__), "..", "resources/TB_word_tokenizer.json"
    )
)
TOKENIZER_POS_LOC = os.path.abspath(
    os.path.join(
        os.path.dirname(__file__),
        "..",
        "part_of_speech_tagger/resources/PoS_label_tokenizer.json",
    )
)  # using the tokenizer of part_of_speech_tagger
TOKENIZER_TAG_LOC = os.path.abspath(
    os.path.join(
        os.path.dirname(__file__),
        "..",
        "stemmer_morph_analyzer/resources/Stemmer_morph_tag_tokenizer.json",
    )
)  # using the tokenizer of stemmer_morph_analyzer
TOKENIZER_LABEL_LOC = RESOURCES_PATH + "DP_label_tokenizer.json"

# Data Preprocessing Config
SENTENCE_MAX_LEN = 40
TAG_MAX_LEN = 15

WORD_OOV_TOKEN = "<OOV>"

# Loading Tokenizers
# Have to load tokenizers here because model config depends on them
tokenizer_word = load_keras_tokenizer(TOKENIZER_WORD_LOC)
tokenizer_pos = load_keras_tokenizer(TOKENIZER_POS_LOC)
tokenizer_tag = load_keras_tokenizer(TOKENIZER_TAG_LOC)
tokenizer_label = load_keras_tokenizer(TOKENIZER_LABEL_LOC)

LABEL_VOCAB_SIZE = len(tokenizer_label.word_index)
POS_VOCAB_SIZE = len(tokenizer_pos.word_index)

# Model Config
WORD_EMBEDDING_VECTOR_SIZE = 128  # Word2Vec_medium.model
WORD_EMBEDDING_VOCAB_SIZE = 63_992  # Word2Vec_medium.model
# WORD_EMBEDDING_MATRIX and TAG_EMBEDDING MATRIX are initialized as Zeros, will be overwritten when model is loaded.
WORD_EMBEDDING_MATRIX = np.zeros(
    (WORD_EMBEDDING_VOCAB_SIZE, WORD_EMBEDDING_VECTOR_SIZE)
)
TAG_EMBEDDING_MATRIX = np.zeros((127, 32))
POS_EMBEDDING_VECTOR_SIZE = 8

NUM_RNN_STACKS = 2
RNN_UNITS_MULTIPLIER = 3
TAG_NUM_RNN_UNITS = WORD_EMBEDDING_VECTOR_SIZE
LC_NUM_RNN_UNITS = TAG_NUM_RNN_UNITS * RNN_UNITS_MULTIPLIER
LC_ARC_LABEL_NUM_RNN_UNITS = TAG_NUM_RNN_UNITS * RNN_UNITS_MULTIPLIER
RC_NUM_RNN_UNITS = TAG_NUM_RNN_UNITS * RNN_UNITS_MULTIPLIER
ARC_LABEL_VECTOR_LEN = SENTENCE_MAX_LEN + 1 + LABEL_VOCAB_SIZE + 1
FC_UNITS_MULTIPLIERS = (8, 4)
WORD_FORM = "whole"
DROPOUT = 0.2


[docs]class TreeStackDP: """ Tree-stack Dependency Parser class. - This dependency parser is *inspired* by `Tree-stack LSTM in Transition Based Dependency Parsing <https://aclanthology.org/K18-2012/>`_. - "Inspire" is emphasized because this implementation uses the approach of using Morphological Tags, Pre-trained word embeddings and POS tags as input for the model, rather than implementing the exact network proposed in the paper. - It achieves 0.6914 LAS (Labeled Attachment Score) and 0.8048 UAS (Unlabeled Attachment Score) on all of test sets of Universal Dependencies 2.9. - Input data is processed by NLTK.tokenize.TreebankWordTokenizer. - For more details about the training procedure, dataset and evaluation metrics, see `ReadMe <https://github.com/vngrs-ai/VNLP/blob/main/vnlp/dependency_parser/ReadMe.md>`_. """ def __init__(self, evaluate): self.model = create_dependency_parser_model( WORD_EMBEDDING_VOCAB_SIZE, WORD_EMBEDDING_VECTOR_SIZE, WORD_EMBEDDING_MATRIX, POS_VOCAB_SIZE, POS_EMBEDDING_VECTOR_SIZE, SENTENCE_MAX_LEN, TAG_MAX_LEN, ARC_LABEL_VECTOR_LEN, NUM_RNN_STACKS, TAG_NUM_RNN_UNITS, LC_NUM_RNN_UNITS, LC_ARC_LABEL_NUM_RNN_UNITS, RC_NUM_RNN_UNITS, DROPOUT, TAG_EMBEDDING_MATRIX, FC_UNITS_MULTIPLIERS, ) # Check and download word embedding matrix and model weights check_and_download( WORD_EMBEDDING_MATRIX_LOC, WORD_EMBEDDING_MATRIX_LINK ) if evaluate: MODEL_WEIGHTS_LOC = EVAL_WEIGHTS_LOC MODEL_WEIGHTS_LINK = EVAL_WEIGHTS_LINK else: MODEL_WEIGHTS_LOC = PROD_WEIGHTS_LOC MODEL_WEIGHTS_LINK = PROD_WEIGHTS_LINK check_and_download(MODEL_WEIGHTS_LOC, MODEL_WEIGHTS_LINK) # Load Word embedding matrix word_embedding_matrix = np.load(WORD_EMBEDDING_MATRIX_LOC) # Load Model weights with open(MODEL_WEIGHTS_LOC, "rb") as fp: model_weights = pickle.load(fp) # Insert word embedding weights to correct position (1 for TreeStack Dependency Parsing model) model_weights.insert(1, word_embedding_matrix) # Set model weights self.model.set_weights(model_weights) self.tokenizer_word = tokenizer_word self.tokenizer_tag = tokenizer_tag self.tokenizer_pos = tokenizer_pos self.tokenizer_label = tokenizer_label # I don't want StemmerAnalyzer and PosTagger to occupy any memory in GPU! with tf.device("/cpu:0"): stemmer_analyzer = StemmerAnalyzer() self.stemmer_analyzer = stemmer_analyzer # stemmer_analyzer is passed to PoSTagger to prevent chain stemmer_analyzer initializations pos_tagger = PoSTagger( "TreeStackPoS", evaluate, self.stemmer_analyzer ) self.pos_tagger = pos_tagger
[docs] def predict( self, sentence: str, displacy_format: bool = False, *args ) -> List[Tuple[int, str, int, str]]: """ Args: sentence: Input sentence. displacy_format: When set True, returns the result in spacy.displacy format to allow visualization. Returns: List of (token_index, token, arc, label). Raises: ValueError: Sentence is too long. Try again by splitting it into smaller pieces. """ sentence_word_punct_tokenized = TreebankWordTokenize(sentence) sentence_analysis_result = self.stemmer_analyzer.predict(sentence) sentence_analysis_result = [ sentence_analysis.replace("^", "+") for sentence_analysis in sentence_analysis_result ] num_tokens_in_sentence = len(sentence_word_punct_tokenized) if num_tokens_in_sentence > SENTENCE_MAX_LEN: raise ValueError( "Sentence is too long. Try again by splitting it into smaller pieces." ) # This is for debugging purposes in case a consistency occurs during tokenization. if not len(sentence_analysis_result) == num_tokens_in_sentence: raise Exception( sentence, "Length of sentence and sentence_analysis_result don't match", ) # *args exist for API compatability. pos_result is overwritten here. pos_result = self.pos_tagger.predict(sentence) pos_tags = [pos for (word, pos) in pos_result] arcs = [] labels = [] for t in range(num_tokens_in_sentence): # t is the index of token/word X = process_single_word_input( t, sentence, sentence_analysis_result, SENTENCE_MAX_LEN, TAG_MAX_LEN, ARC_LABEL_VECTOR_LEN, self.tokenizer_word, self.tokenizer_tag, self.tokenizer_label, self.tokenizer_pos, arcs, labels, pos_tags, WORD_FORM, ) # Predicting logits = self.model(X).numpy()[0] arc, label = decode_arc_label_vector( logits, SENTENCE_MAX_LEN, LABEL_VOCAB_SIZE ) arcs.append(arc) labels.append(label) # 0 arc index is reserved for root, therefore arc = 1 means word is dependent on the first word dp_result = [] for idx, word in enumerate(sentence_word_punct_tokenized): dp_result.append( ( idx + 1, word, arcs[idx], self.tokenizer_label.sequences_to_texts([[labels[idx]]])[ 0 ], ) ) if not displacy_format: return dp_result else: dp_result_displacy_format = dp_pos_to_displacy_format( dp_result, pos_result ) return dp_result_displacy_format