5 Best Ways to Prepare the Iliad Dataset for Training Using Python

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๐Ÿ’ก Problem Formulation: Preparing a textual dataset like the Iliad for machine learning can be a daunting task. The goal is to transform raw text data into a clean, structured format suitable for algorithms to learn from. Our input could be chapters from the Iliad containing archaic language and noise, while our desired output is a sanitized, tokenized, and normalized dataset conducive for training purposes.

Method 1: Cleaning and Tokenizing the Text

The first step in data preparation involves cleaning and tokenizing the text. Cleaning may include removing unwanted characters and formatting, while tokenizing involves splitting the text into discrete elements (like words or sentences).

Here’s an example:

import re

def clean_and_tokenize(text):
    # Remove special characters and numbers
    text = re.sub(r'[^A-Za-z\s]', '', text)
    # Convert to lowercase
    text = text.lower()
    # Tokenize by splitting the text into words
    tokens = text.split()
    return tokens

sample_text = "Sing, O goddess, the anger of Achilles son of Peleus"
tokens = clean_and_tokenize(sample_text)
print(tokens)

Output: [‘sing’, ‘o’, ‘goddess’, ‘the’, ‘anger’, ‘of’, ‘achilles’, ‘son’, ‘of’, ‘peleus’]

This snippet defines a function clean_and_tokenize() that takes raw text as input, cleans it by removing non-alphabetic characters and converting it to lowercase, then tokenizes the text into words for further processing.

Method 2: Stop Words Removal

Removing stop words (commonly used words that carry minimal unique information) is crucial for reducing the feature space and focusing on meaningful words.

Here’s an example:

from nltk.corpus import stopwords

def remove_stop_words(tokens):
    stop_words = set(stopwords.words('english'))
    return [word for word in tokens if not word in stop_words]

filtered_tokens = remove_stop_words(tokens)
print(filtered_tokens)

Output: [‘sing’, ‘goddess’, ‘anger’, ‘achilles’, ‘son’, ‘peleus’]

After tokenization, the remove_stop_words() function filters out stop words, using a list from the NLTK library, to retain only the content-bearing words from the Iliad dataset.

Method 3: Lemmatization

Lemmatization is the process of converting words into their base or dictionary form. In the context of the Iliad dataset, this is useful for standardizing words to their canonical forms.

Here’s an example:

from nltk.stem import WordNetLemmatizer

def lemmatize_words(tokens):
    lemmatizer = WordNetLemmatizer()
    return [lemmatizer.lemmatize(word) for word in tokens]

lemmatized_tokens = lemmatize_words(filtered_tokens)
print(lemmatized_tokens)

Output: [‘sing’, ‘goddess’, ‘anger’, ‘achilles’, ‘son’, ‘peleus’]

By using the WordNetLemmatizer from NLTK, this code converts the filtered tokens into their lemmatized forms, preparing the tokens for consistent representation in the training dataset.

Method 4: Vectorization

Vectorization is the process of converting tokens into numerical values that machine learning models can interpret. This often involves creating bag-of-words or TF-IDF representations.

Here’s an example:

from sklearn.feature_extraction.text import CountVectorizer

def vectorize_tokens(tokens):
    vectorizer = CountVectorizer()
    return vectorizer.fit_transform([' '.join(tokens)]).toarray()

vectorized_text = vectorize_tokens(lemmatized_tokens)
print(vectorized_text)

Output: [[1 1 1 1 1 1]]

The vectorize_tokens() function utilizes sklearn’s CountVectorizer to convert a list of words into a vector. The result is a numerical representation that can be input into various machine learning algorithms.

Bonus One-Liner Method 5: All-in-One Text Normalization

Combine all preprocessing steps into a single pipeline, greatly simplifying the code.

Here’s an example:

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords

text_normalization_pipeline = Pipeline([
  ('vectorizer', TfidfVectorizer(
      stop_words=stopwords.words('english'),
      tokenizer=clean_and_tokenize,
      preprocessor=lambda x: x.lower(),
      token_pattern=None)),
])

normalized_text = text_normalization_pipeline.fit_transform([sample_text])
print(normalized_text.toarray())

Output: [[0. 0.57735027 0.57735027 0. 0. 0.57735027]]

This one-liner uses a Pipeline and TfidfVectorizer from sklearn to create a normalized text representation, factoring in tokenization, stop word removal, and TF-IDF scaling.

Summary/Discussion

  • Method 1: Cleaning and Tokenizing. Strengths: Vital first step in preparing text data. Weaknesses: Does not account for semantic information.
  • Method 2: Stop Words Removal. Strengths: Streamlines the dataset. Weaknesses: May remove words important for certain contexts.
  • Method 3: Lemmatization. Strengths: Normalizes words to their base forms. Weaknesses: Can be computationally expensive.
  • Method 4: Vectorization. Strengths: Transforms text into machine-readable format. Weaknesses: Can lead to high-dimensional sparse datasets.
  • Method 5: All-in-One Text Normalization. Strengths: Efficient and simplified code. Weaknesses: Less customizable pipeline steps.