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Spelling Correction with The Pretrained BERT Model

Stephen Cheng

Intro

BERT (Bidirectional Encoder Representations from Transformers) is published by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering, Natural Language Inference, and others. BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. This is in contrast to previous efforts which looked at a text sequence either from left to right or combined left-to-right and right-to-left training. The results show that a language model which is bidirectionally trained can have a deeper sense of language context and flow than single-direction language models.

BERT makes use of Transformer, an attention mechanism that learns contextual relations between words (or sub-words) in a text. Transformer includes two separate mechanisms — an encoder that reads the text input and a decoder that produces a prediction for the task. As opposed to directional models, which read the text input sequentially (left-to-right or right-to-left), the Transformer encoder reads the entire sequence of words at once. Therefore it is considered bidirectional, though it would be more accurate to say that it’s non-directional. This characteristic allows the model to learn the context of a word based on all of its surroundings (left and right of the word).

Demo

Import necessary libraris

import re
import nltk
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from pytesseract import image_to_string
from enchant.checker import SpellChecker
from difflib import SequenceMatcher
import torch
from pytorch_pretrained_bert import BertTokenizer, BertForMaskedLM

Process images by using OCR

imagename = '1.png'
pil_img = Image.open(imagename)
text = image_to_string(pil_img)
text_original = str(text)

print(text)
plt.figure(figsize = (12,4))
plt.imshow(np.asarray(pil_img))

Output:

1
2
3

national economy gained momentum in recent weeks as con@gmer spending
Strengthened, manufacturing activity cont@™ed to rise, and producers
scheduled more investment in plant and equipment.

Process text and mask incorrect words

# text cleanup
rep = {'\n': ' ',
       '\\': ' ',
       '\"': '"',
       '-': ' ',
       '"': ' " ',
       ',':' , ',
       '.':' . ',
       '!':' ! ',
       '?':' ? ',
       "n't": " not",
       "'ll": " will",
       '*':' * ',
       '(': ' ( ',
       ')': ' ) ',
       "s'": "s '"}

rep = dict((re.escape(k), v) for k, v in rep.items())
pattern = re.compile("|".join(rep.keys()))
text = pattern.sub(lambda m: rep[re.escape(m.group(0))], text)
def get_personslist(text):
    personslist = []
    for sent in nltk.sent_tokenize(text):
        for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
            if isinstance(chunk, nltk.tree.Tree) and chunk.label() == 'PERSON':
                personslist.insert(0, (chunk.leaves()[0][0]))
    return list(set(personslist))
personslist = get_personslist(text)
ignorewords = personslist + ["!", ",", ".", "\"", "?", '(', ')', '*', "''"]

# use SpellChecker to find incorrect words
d = SpellChecker("en_US")
words = text.split()
incorrectwords = [w for w in words if not d.check(w) and w not in ignorewords]

# use SpellChecker to get suggested replacements
suggestedwords = [d.suggest(w) for w in incorrectwords]

# replace incorrect words with [MASK]
for w in incorrectwords:
    text = text.replace(w, '[MASK]')

print(text)

Output:

1	national economy gained momentum in recent weeks as [MASK] spending Strengthened , manufacturing activity [MASK] to rise , and producers scheduled more investment in plant and equipment .

Use the pretrained BERT model to predict words

# Tokenize text
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
MASKIDS = [i for i, e in enumerate(tokenized_text) if e == '[MASK]']

# Create the segments tensors
segs = [i for i, e in enumerate(tokenized_text) if e == "."]
segments_ids = []
prev = -1
for k, s in enumerate(segs):
    segments_ids = segments_ids + [k] * (s-prev)
    prev = s
segments_ids = segments_ids + [len(segs)] * (len(tokenized_text) - len(segments_ids))
segments_tensors = torch.tensor([segments_ids])

# prepare Torch inputs
tokens_tensors = torch.tensor([indexed_tokens])

# Load pre-trained model
model = BertForMaskedLM.from_pretrained('bert-base-uncased')

# Predict all tokens
with torch.no_grad():
    predictions = model(tokens_tensors, segments_tensors)

Match with proposals from SpellChecker

def predict_word(text_original, predictions, MASKIDS):
    pred_words = []
    for i in range(len(MASKIDS)):
        preds = torch.topk(predictions[0, MASKIDS[i]], k=50)
        indices = preds.indices.tolist()
        pred_list = tokenizer.convert_ids_to_tokens(indices)
        sugg_list = suggestedwords[i]
        sim_max = 0
        predicted_token = ''
        for word1 in pred_list:
            for word2 in sugg_list:
                s = SequenceMatcher(None, word1, word2).ratio()
                if s is not None and s > sim_max:
                    sim_max = s
                    predicted_token = word1
        text_original = text_original.replace('[MASK]', predicted_token, 1)
    return text_original

1 2	text_refined = predict_word(text, predictions, MASKIDS) print(text_refined)

Output:

1	national economy gained momentum in recent weeks as consumer spending Strengthened , manufacturing activity continued to rise , and producers scheduled more investment in plant and equipment .

BERT, Spelling-Correction — Jul 18, 2020

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