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| """ | |
| This module contains utils for preprocessing the text before converting it to embeddings. | |
| - TextPreprocessorBuilder preprocesses individual strings. | |
| * lowering cases | |
| * converting numbers to words or characters | |
| * merging and stripping spaces | |
| * removing punctuation | |
| * removing stop words | |
| * lemmatizing | |
| * removing specific parts of speech (adverbs and interjections) | |
| - TextSummarizer extracts the most important sentences from a long string using text-ranking. | |
| """ | |
| import math | |
| import re | |
| import string | |
| import nltk | |
| import spacy | |
| from nltk.corpus import stopwords | |
| from nltk.stem import WordNetLemmatizer | |
| from num2words import num2words | |
| class TextPreprocessorBuilder: | |
| # Define class variables as None initially | |
| _stop_words = set(stopwords.words('english')) | |
| _lemmatizer = WordNetLemmatizer() | |
| # Some of the functions are expensive. We cache the results. | |
| _lemmatizer_cache = {} | |
| _pos_remove_cache = {} | |
| def __init__(self, text: str): | |
| self.text = text | |
| def to_lower(self): | |
| # Match both words and non-word characters | |
| tokens = re.findall(r'\b\w+\b|\W+', self.text) | |
| for i, token in enumerate(tokens): | |
| # Check if token is a word | |
| if re.match(r'^\w+$', token): | |
| # Check if token is not an abbreviation or constant | |
| if not re.match(r'^[A-Z]+$', token) and not re.match(r'^[A-Z_]+$', token): | |
| tokens[i] = token.lower() | |
| self.text = "".join(tokens) | |
| return self | |
| def num_to_word(self, min_len: int = 1): | |
| # Match both words and non-word characters | |
| tokens = re.findall(r'\b\w+\b|\W+', self.text) | |
| for i, token in enumerate(tokens): | |
| # Check if token is a number of length `min_len` or more | |
| if token.isdigit() and len(token) >= min_len: | |
| # This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers) | |
| # 740700 will become "seven hundred and forty thousand seven hundred". | |
| tokens[i] = num2words(int(token)).replace(",", "") # Remove commas from num2words. | |
| self.text = "".join(tokens) | |
| return self | |
| def num_to_char_long(self, min_len: int = 1): | |
| # Match both words and non-word characters | |
| tokens = re.findall(r'\b\w+\b|\W+', self.text) | |
| for i, token in enumerate(tokens): | |
| # Check if token is a number of length `min_len` or more | |
| if token.isdigit() and len(token) >= min_len: | |
| # This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers) | |
| # 740700 will become HHHHHHEEEEEAAAAHHHAAA | |
| def convert_token(token): | |
| return ''.join((chr(int(digit) + 65) * (i + 1)) for i, digit in enumerate(token[::-1]))[::-1] | |
| tokens[i] = convert_token(tokens[i]) | |
| self.text = "".join(tokens) | |
| return self | |
| def num_to_char(self, min_len: int = 1): | |
| # Match both words and non-word characters | |
| tokens = re.findall(r'\b\w+\b|\W+', self.text) | |
| for i, token in enumerate(tokens): | |
| # Check if token is a number of length `min_len` or more | |
| if token.isdigit() and len(token) >= min_len: | |
| # This is done to pay better attention to numbers (e.g. ticket numbers, thread numbers, post numbers) | |
| # 740700 will become HEAHAA | |
| tokens[i] = ''.join(chr(int(digit) + 65) for digit in token) | |
| self.text = "".join(tokens) | |
| return self | |
| def merge_spaces(self): | |
| self.text = re.sub(' +', ' ', self.text) | |
| return self | |
| def strip(self): | |
| self.text = self.text.strip() | |
| return self | |
| def remove_punctuation(self): | |
| self.text = self.text.translate(str.maketrans('', '', string.punctuation)) | |
| return self | |
| def remove_stopwords(self): | |
| self.text = "".join([word for word in re.findall(r'\b\w+\b|\W+', self.text) if word not in TextPreprocessorBuilder._stop_words]) | |
| return self | |
| def remove_specific_pos(self): | |
| """ | |
| In the English language, adverbs and interjections rarely provide meaningul information. | |
| Removing them improves the embedding precision. Don't tell JK Rowling, though. | |
| """ | |
| processed_text = TextPreprocessorBuilder._pos_remove_cache.get(self.text) | |
| if processed_text: | |
| self.text = processed_text | |
| return self | |
| # Match both words and non-word characters | |
| tokens = re.findall(r'\b\w+\b|\W+', self.text) | |
| # Exclude adverbs and interjections | |
| excluded_tags = ['RB', 'RBR', 'RBS', 'UH'] | |
| for i, token in enumerate(tokens): | |
| # Check if token is a word | |
| if re.match(r'^\w+$', token): | |
| # Part-of-speech tag the word | |
| pos = nltk.pos_tag([token])[0][1] | |
| # If the word's POS tag is in the excluded list, remove the word | |
| if pos in excluded_tags: | |
| tokens[i] = '' | |
| new_text = "".join(tokens) | |
| TextPreprocessorBuilder._pos_remove_cache[self.text] = new_text | |
| self.text = new_text | |
| return self | |
| def lemmatize(self): | |
| processed_text = TextPreprocessorBuilder._lemmatizer_cache.get(self.text) | |
| if processed_text: | |
| self.text = processed_text | |
| return self | |
| new_text = "".join([TextPreprocessorBuilder._lemmatizer.lemmatize(word) for word in re.findall(r'\b\w+\b|\W+', self.text)]) | |
| TextPreprocessorBuilder._lemmatizer_cache[self.text] = new_text | |
| self.text = new_text | |
| return self | |
| def build(self): | |
| return self.text | |
| class TextSummarizer: | |
| _nlp_pipeline = None | |
| _cache = {} | |
| def _load_nlp_pipeline(): | |
| # Lazy-load it. | |
| if TextSummarizer._nlp_pipeline is None: | |
| TextSummarizer._nlp_pipeline = spacy.load('en_core_web_sm') | |
| TextSummarizer._nlp_pipeline.add_pipe("textrank", last=True) | |
| return TextSummarizer._nlp_pipeline | |
| def process_long_text(text: str, min_num_sent: int) -> list[str]: | |
| """ | |
| This function applies a text summarization process on a given text string, extracting | |
| the most important sentences based on the principle that 20% of the content is responsible | |
| for 80% of the meaning (the Pareto Principle). | |
| Returns: | |
| list: A list of the most important sentences | |
| """ | |
| # Attempt to get the result from cache | |
| cache_key = (text, min_num_sent) | |
| cached_result = TextSummarizer._cache.get(cache_key, None) | |
| if cached_result is not None: | |
| return cached_result | |
| nlp_pipeline = TextSummarizer._load_nlp_pipeline() | |
| doc = nlp_pipeline(text) | |
| num_sent = len(list(doc.sents)) | |
| result = [] | |
| if num_sent >= min_num_sent: | |
| limit_phrases = math.ceil(len(doc._.phrases) * 0.20) # 20% of the phrases, rounded up | |
| limit_sentences = math.ceil(num_sent * 0.20) # 20% of the sentences, rounded up | |
| result = [str(sent) for sent in doc._.textrank.summary(limit_phrases=limit_phrases, limit_sentences=limit_sentences)] | |
| else: | |
| result = [text] | |
| # Store the result in cache before returning it | |
| TextSummarizer._cache[cache_key] = result | |
| return result | |