Chapter 1: Introduction to Natural Language Processing
1.3 Traditional Methods in NLP
Before the advent of deep learning and transformer models, a variety of traditional methods were used to tackle NLP tasks. These methods, which include rule-based systems, statistical methods, and classical machine learning algorithms, laid the groundwork for modern NLP.
It is worth noting that these traditional methods were not without their limitations. Rule-based systems often struggled with processing colloquial language, while statistical models were heavily reliant on hand-crafted features that required significant domain expertise. Classical machine learning algorithms also had their fair share of challenges, including the need for large amounts of labeled data and difficulty with handling high-dimensional feature spaces.
Despite these limitations, traditional NLP methods played an instrumental role in advancing the field and paved the way for the emergence of deep learning and transformer models. These modern approaches have since revolutionized NLP, enabling the development of sophisticated language models that can perform a wide range of tasks, from sentiment analysis to machine translation and beyond. However, it is important to acknowledge the contributions of traditional NLP methods and recognize the role they played in shaping the field as we know it today.
1.3.1 Rule-Based Systems
In the early days of NLP, rule-based systems were prevalent. These systems used manually crafted rules to understand and generate language. However, as technology advanced, machine learning algorithms were developed, allowing for more sophisticated natural language processing.
These algorithms use statistical models and neural networks to learn from large amounts of data, enabling them to understand language patterns and generate more accurate and nuanced language. As a result, rule-based systems have largely been replaced by these more advanced machine learning models, which continue to improve and evolve.
Despite this shift, rule-based systems still have their place in certain NLP applications, particularly in cases where accuracy and interpretability are crucial.
For example, a rule-based system for a task like part-of-speech tagging might have rules like:
- If the word is 'is', 'am', or 'are', label it as a verb.
- If the word ends with '-ly', label it as an adverb.
Here's a simple example of a rule-based system for sentiment analysis:
# Rule-based sentiment analysis
def rule_based_sentiment_analysis(text):
positive_words = ['love', 'like', 'enjoy', 'happy', 'joy']
negative_words = ['hate', 'dislike', 'sad', 'angry', 'bad']
positive_score = sum(word in text for word in positive_words)
negative_score = sum(word in text for word in negative_words)
return positive_score - negative_score
# Test the function
text = "I love this book. It's amazing!"
print(rule_based_sentiment_analysis(text.split()))In this code, we've created a simple rule-based system that counts the number of positive and negative words in a text to determine its sentiment.
While rule-based systems are straightforward and easy to interpret, they're limited in their ability to handle the complexity and ambiguity of natural language. They also require a lot of manual effort to create and maintain, and they don't generalize well to unseen data or different domains.
1.3.2 Statistical Methods
To overcome the limitations of rule-based systems, researchers began to use statistical methods for NLP. These methods, like Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), use probabilities and mathematical models to understand and generate language.
Statistical methods have been a major breakthrough in NLP research, as they enable computers to learn from data and improve their performance over time. By analyzing large amounts of text data, statistical models can identify patterns and relationships between words, which can then be used to make predictions about new text.
For example, an HMM for part-of-speech tagging might learn the probability of a noun following a verb or an adjective following an adverb. These models are trained on large amounts of annotated text, which enables them to learn the patterns of language use in a given domain. As a result, statistical methods have become increasingly popular in NLP research and are widely used in applications such as machine translation, sentiment analysis, and text classification.
Example:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# For this example, let's imagine we have a corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# We'll use CountVectorizer to convert the text data into a matrix of token counts
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train a Naive Bayes classifier
clf = MultinomialNB()
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we first convert our reviews into a matrix of token counts using CountVectorizer. Then, we split our data into a training set and a testing set. We use the training set to train a MultinomialNB classifier, which is a type of Naive Bayes classifier suitable for classification with discrete features such as word counts. Finally, we test the classifier on the testing set and print the accuracy score.
While statistical methods are more powerful than rule-based systems and can generalize to unseen data, they still have limitations. They often fail to capture the complex relationships and structures in language, and they require a large amount of annotated data.
1.3.3 Classical Machine Learning
With the advent of machine learning, researchers started applying ML algorithms to NLP tasks. These methods, like Support Vector Machines (SVMs) and decision trees, could learn complex patterns from data and didn't require explicit rules.
However, classical machine learning methods require feature engineering, i.e., the manual process of providing the algorithm with the right kind of input data. For example, for text classification, you might create features like the count of specific words, the length of the sentences, the use of capital letters, and so on.
Here's an example of using a machine learning method (SVM) for sentiment analysis:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
# Use the same corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# Use TfidfVectorizer to convert the text data into a matrix of TF-IDF features
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train an SVM classifier
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we follow a similar procedure, but instead of using CountVectorizer, we use TfidfVectorizer, which converts the text data into a matrix of TF-IDF features. These features not only count the frequency of each word in each document (like CountVectorizer), but they also downscale the weights of words that occur frequently across all documents (which are likely less informative than those that occur only in a smaller portion of the corpus). We then use these features to train an SVM classifier with a linear kernel, and we print the accuracy score of the classifier on the testing set.
While classical machine learning methods can capture more complex patterns than rule-based or statistical methods, they still require a lot of manual effort for feature engineering and do not handle the intricacies of language (like context, idioms, and ambiguities) very well.
1.3 Traditional Methods in NLP
Before the advent of deep learning and transformer models, a variety of traditional methods were used to tackle NLP tasks. These methods, which include rule-based systems, statistical methods, and classical machine learning algorithms, laid the groundwork for modern NLP.
It is worth noting that these traditional methods were not without their limitations. Rule-based systems often struggled with processing colloquial language, while statistical models were heavily reliant on hand-crafted features that required significant domain expertise. Classical machine learning algorithms also had their fair share of challenges, including the need for large amounts of labeled data and difficulty with handling high-dimensional feature spaces.
Despite these limitations, traditional NLP methods played an instrumental role in advancing the field and paved the way for the emergence of deep learning and transformer models. These modern approaches have since revolutionized NLP, enabling the development of sophisticated language models that can perform a wide range of tasks, from sentiment analysis to machine translation and beyond. However, it is important to acknowledge the contributions of traditional NLP methods and recognize the role they played in shaping the field as we know it today.
1.3.1 Rule-Based Systems
In the early days of NLP, rule-based systems were prevalent. These systems used manually crafted rules to understand and generate language. However, as technology advanced, machine learning algorithms were developed, allowing for more sophisticated natural language processing.
These algorithms use statistical models and neural networks to learn from large amounts of data, enabling them to understand language patterns and generate more accurate and nuanced language. As a result, rule-based systems have largely been replaced by these more advanced machine learning models, which continue to improve and evolve.
Despite this shift, rule-based systems still have their place in certain NLP applications, particularly in cases where accuracy and interpretability are crucial.
For example, a rule-based system for a task like part-of-speech tagging might have rules like:
- If the word is 'is', 'am', or 'are', label it as a verb.
- If the word ends with '-ly', label it as an adverb.
Here's a simple example of a rule-based system for sentiment analysis:
# Rule-based sentiment analysis
def rule_based_sentiment_analysis(text):
positive_words = ['love', 'like', 'enjoy', 'happy', 'joy']
negative_words = ['hate', 'dislike', 'sad', 'angry', 'bad']
positive_score = sum(word in text for word in positive_words)
negative_score = sum(word in text for word in negative_words)
return positive_score - negative_score
# Test the function
text = "I love this book. It's amazing!"
print(rule_based_sentiment_analysis(text.split()))In this code, we've created a simple rule-based system that counts the number of positive and negative words in a text to determine its sentiment.
While rule-based systems are straightforward and easy to interpret, they're limited in their ability to handle the complexity and ambiguity of natural language. They also require a lot of manual effort to create and maintain, and they don't generalize well to unseen data or different domains.
1.3.2 Statistical Methods
To overcome the limitations of rule-based systems, researchers began to use statistical methods for NLP. These methods, like Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), use probabilities and mathematical models to understand and generate language.
Statistical methods have been a major breakthrough in NLP research, as they enable computers to learn from data and improve their performance over time. By analyzing large amounts of text data, statistical models can identify patterns and relationships between words, which can then be used to make predictions about new text.
For example, an HMM for part-of-speech tagging might learn the probability of a noun following a verb or an adjective following an adverb. These models are trained on large amounts of annotated text, which enables them to learn the patterns of language use in a given domain. As a result, statistical methods have become increasingly popular in NLP research and are widely used in applications such as machine translation, sentiment analysis, and text classification.
Example:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# For this example, let's imagine we have a corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# We'll use CountVectorizer to convert the text data into a matrix of token counts
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train a Naive Bayes classifier
clf = MultinomialNB()
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we first convert our reviews into a matrix of token counts using CountVectorizer. Then, we split our data into a training set and a testing set. We use the training set to train a MultinomialNB classifier, which is a type of Naive Bayes classifier suitable for classification with discrete features such as word counts. Finally, we test the classifier on the testing set and print the accuracy score.
While statistical methods are more powerful than rule-based systems and can generalize to unseen data, they still have limitations. They often fail to capture the complex relationships and structures in language, and they require a large amount of annotated data.
1.3.3 Classical Machine Learning
With the advent of machine learning, researchers started applying ML algorithms to NLP tasks. These methods, like Support Vector Machines (SVMs) and decision trees, could learn complex patterns from data and didn't require explicit rules.
However, classical machine learning methods require feature engineering, i.e., the manual process of providing the algorithm with the right kind of input data. For example, for text classification, you might create features like the count of specific words, the length of the sentences, the use of capital letters, and so on.
Here's an example of using a machine learning method (SVM) for sentiment analysis:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
# Use the same corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# Use TfidfVectorizer to convert the text data into a matrix of TF-IDF features
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train an SVM classifier
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we follow a similar procedure, but instead of using CountVectorizer, we use TfidfVectorizer, which converts the text data into a matrix of TF-IDF features. These features not only count the frequency of each word in each document (like CountVectorizer), but they also downscale the weights of words that occur frequently across all documents (which are likely less informative than those that occur only in a smaller portion of the corpus). We then use these features to train an SVM classifier with a linear kernel, and we print the accuracy score of the classifier on the testing set.
While classical machine learning methods can capture more complex patterns than rule-based or statistical methods, they still require a lot of manual effort for feature engineering and do not handle the intricacies of language (like context, idioms, and ambiguities) very well.
1.3 Traditional Methods in NLP
Before the advent of deep learning and transformer models, a variety of traditional methods were used to tackle NLP tasks. These methods, which include rule-based systems, statistical methods, and classical machine learning algorithms, laid the groundwork for modern NLP.
It is worth noting that these traditional methods were not without their limitations. Rule-based systems often struggled with processing colloquial language, while statistical models were heavily reliant on hand-crafted features that required significant domain expertise. Classical machine learning algorithms also had their fair share of challenges, including the need for large amounts of labeled data and difficulty with handling high-dimensional feature spaces.
Despite these limitations, traditional NLP methods played an instrumental role in advancing the field and paved the way for the emergence of deep learning and transformer models. These modern approaches have since revolutionized NLP, enabling the development of sophisticated language models that can perform a wide range of tasks, from sentiment analysis to machine translation and beyond. However, it is important to acknowledge the contributions of traditional NLP methods and recognize the role they played in shaping the field as we know it today.
1.3.1 Rule-Based Systems
In the early days of NLP, rule-based systems were prevalent. These systems used manually crafted rules to understand and generate language. However, as technology advanced, machine learning algorithms were developed, allowing for more sophisticated natural language processing.
These algorithms use statistical models and neural networks to learn from large amounts of data, enabling them to understand language patterns and generate more accurate and nuanced language. As a result, rule-based systems have largely been replaced by these more advanced machine learning models, which continue to improve and evolve.
Despite this shift, rule-based systems still have their place in certain NLP applications, particularly in cases where accuracy and interpretability are crucial.
For example, a rule-based system for a task like part-of-speech tagging might have rules like:
- If the word is 'is', 'am', or 'are', label it as a verb.
- If the word ends with '-ly', label it as an adverb.
Here's a simple example of a rule-based system for sentiment analysis:
# Rule-based sentiment analysis
def rule_based_sentiment_analysis(text):
positive_words = ['love', 'like', 'enjoy', 'happy', 'joy']
negative_words = ['hate', 'dislike', 'sad', 'angry', 'bad']
positive_score = sum(word in text for word in positive_words)
negative_score = sum(word in text for word in negative_words)
return positive_score - negative_score
# Test the function
text = "I love this book. It's amazing!"
print(rule_based_sentiment_analysis(text.split()))In this code, we've created a simple rule-based system that counts the number of positive and negative words in a text to determine its sentiment.
While rule-based systems are straightforward and easy to interpret, they're limited in their ability to handle the complexity and ambiguity of natural language. They also require a lot of manual effort to create and maintain, and they don't generalize well to unseen data or different domains.
1.3.2 Statistical Methods
To overcome the limitations of rule-based systems, researchers began to use statistical methods for NLP. These methods, like Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), use probabilities and mathematical models to understand and generate language.
Statistical methods have been a major breakthrough in NLP research, as they enable computers to learn from data and improve their performance over time. By analyzing large amounts of text data, statistical models can identify patterns and relationships between words, which can then be used to make predictions about new text.
For example, an HMM for part-of-speech tagging might learn the probability of a noun following a verb or an adjective following an adverb. These models are trained on large amounts of annotated text, which enables them to learn the patterns of language use in a given domain. As a result, statistical methods have become increasingly popular in NLP research and are widely used in applications such as machine translation, sentiment analysis, and text classification.
Example:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# For this example, let's imagine we have a corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# We'll use CountVectorizer to convert the text data into a matrix of token counts
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train a Naive Bayes classifier
clf = MultinomialNB()
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we first convert our reviews into a matrix of token counts using CountVectorizer. Then, we split our data into a training set and a testing set. We use the training set to train a MultinomialNB classifier, which is a type of Naive Bayes classifier suitable for classification with discrete features such as word counts. Finally, we test the classifier on the testing set and print the accuracy score.
While statistical methods are more powerful than rule-based systems and can generalize to unseen data, they still have limitations. They often fail to capture the complex relationships and structures in language, and they require a large amount of annotated data.
1.3.3 Classical Machine Learning
With the advent of machine learning, researchers started applying ML algorithms to NLP tasks. These methods, like Support Vector Machines (SVMs) and decision trees, could learn complex patterns from data and didn't require explicit rules.
However, classical machine learning methods require feature engineering, i.e., the manual process of providing the algorithm with the right kind of input data. For example, for text classification, you might create features like the count of specific words, the length of the sentences, the use of capital letters, and so on.
Here's an example of using a machine learning method (SVM) for sentiment analysis:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
# Use the same corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# Use TfidfVectorizer to convert the text data into a matrix of TF-IDF features
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train an SVM classifier
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we follow a similar procedure, but instead of using CountVectorizer, we use TfidfVectorizer, which converts the text data into a matrix of TF-IDF features. These features not only count the frequency of each word in each document (like CountVectorizer), but they also downscale the weights of words that occur frequently across all documents (which are likely less informative than those that occur only in a smaller portion of the corpus). We then use these features to train an SVM classifier with a linear kernel, and we print the accuracy score of the classifier on the testing set.
While classical machine learning methods can capture more complex patterns than rule-based or statistical methods, they still require a lot of manual effort for feature engineering and do not handle the intricacies of language (like context, idioms, and ambiguities) very well.
1.3 Traditional Methods in NLP
Before the advent of deep learning and transformer models, a variety of traditional methods were used to tackle NLP tasks. These methods, which include rule-based systems, statistical methods, and classical machine learning algorithms, laid the groundwork for modern NLP.
It is worth noting that these traditional methods were not without their limitations. Rule-based systems often struggled with processing colloquial language, while statistical models were heavily reliant on hand-crafted features that required significant domain expertise. Classical machine learning algorithms also had their fair share of challenges, including the need for large amounts of labeled data and difficulty with handling high-dimensional feature spaces.
Despite these limitations, traditional NLP methods played an instrumental role in advancing the field and paved the way for the emergence of deep learning and transformer models. These modern approaches have since revolutionized NLP, enabling the development of sophisticated language models that can perform a wide range of tasks, from sentiment analysis to machine translation and beyond. However, it is important to acknowledge the contributions of traditional NLP methods and recognize the role they played in shaping the field as we know it today.
1.3.1 Rule-Based Systems
In the early days of NLP, rule-based systems were prevalent. These systems used manually crafted rules to understand and generate language. However, as technology advanced, machine learning algorithms were developed, allowing for more sophisticated natural language processing.
These algorithms use statistical models and neural networks to learn from large amounts of data, enabling them to understand language patterns and generate more accurate and nuanced language. As a result, rule-based systems have largely been replaced by these more advanced machine learning models, which continue to improve and evolve.
Despite this shift, rule-based systems still have their place in certain NLP applications, particularly in cases where accuracy and interpretability are crucial.
For example, a rule-based system for a task like part-of-speech tagging might have rules like:
- If the word is 'is', 'am', or 'are', label it as a verb.
- If the word ends with '-ly', label it as an adverb.
Here's a simple example of a rule-based system for sentiment analysis:
# Rule-based sentiment analysis
def rule_based_sentiment_analysis(text):
positive_words = ['love', 'like', 'enjoy', 'happy', 'joy']
negative_words = ['hate', 'dislike', 'sad', 'angry', 'bad']
positive_score = sum(word in text for word in positive_words)
negative_score = sum(word in text for word in negative_words)
return positive_score - negative_score
# Test the function
text = "I love this book. It's amazing!"
print(rule_based_sentiment_analysis(text.split()))In this code, we've created a simple rule-based system that counts the number of positive and negative words in a text to determine its sentiment.
While rule-based systems are straightforward and easy to interpret, they're limited in their ability to handle the complexity and ambiguity of natural language. They also require a lot of manual effort to create and maintain, and they don't generalize well to unseen data or different domains.
1.3.2 Statistical Methods
To overcome the limitations of rule-based systems, researchers began to use statistical methods for NLP. These methods, like Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), use probabilities and mathematical models to understand and generate language.
Statistical methods have been a major breakthrough in NLP research, as they enable computers to learn from data and improve their performance over time. By analyzing large amounts of text data, statistical models can identify patterns and relationships between words, which can then be used to make predictions about new text.
For example, an HMM for part-of-speech tagging might learn the probability of a noun following a verb or an adjective following an adverb. These models are trained on large amounts of annotated text, which enables them to learn the patterns of language use in a given domain. As a result, statistical methods have become increasingly popular in NLP research and are widely used in applications such as machine translation, sentiment analysis, and text classification.
Example:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# For this example, let's imagine we have a corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# We'll use CountVectorizer to convert the text data into a matrix of token counts
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train a Naive Bayes classifier
clf = MultinomialNB()
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we first convert our reviews into a matrix of token counts using CountVectorizer. Then, we split our data into a training set and a testing set. We use the training set to train a MultinomialNB classifier, which is a type of Naive Bayes classifier suitable for classification with discrete features such as word counts. Finally, we test the classifier on the testing set and print the accuracy score.
While statistical methods are more powerful than rule-based systems and can generalize to unseen data, they still have limitations. They often fail to capture the complex relationships and structures in language, and they require a large amount of annotated data.
1.3.3 Classical Machine Learning
With the advent of machine learning, researchers started applying ML algorithms to NLP tasks. These methods, like Support Vector Machines (SVMs) and decision trees, could learn complex patterns from data and didn't require explicit rules.
However, classical machine learning methods require feature engineering, i.e., the manual process of providing the algorithm with the right kind of input data. For example, for text classification, you might create features like the count of specific words, the length of the sentences, the use of capital letters, and so on.
Here's an example of using a machine learning method (SVM) for sentiment analysis:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
# Use the same corpus of movie reviews
reviews = ['I love this movie', 'I hate this movie', 'This movie is amazing', 'This movie is terrible']
sentiments = [1, 0, 1, 0] # 1 for positive, 0 for negative
# Use TfidfVectorizer to convert the text data into a matrix of TF-IDF features
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(reviews)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
# Train an SVM classifier
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# Predict the sentiment of unseen reviews
predictions = clf.predict(X_test)
# Print the accuracy score
print("Accuracy:", accuracy_score(y_test, predictions))In this example, we follow a similar procedure, but instead of using CountVectorizer, we use TfidfVectorizer, which converts the text data into a matrix of TF-IDF features. These features not only count the frequency of each word in each document (like CountVectorizer), but they also downscale the weights of words that occur frequently across all documents (which are likely less informative than those that occur only in a smaller portion of the corpus). We then use these features to train an SVM classifier with a linear kernel, and we print the accuracy score of the classifier on the testing set.
While classical machine learning methods can capture more complex patterns than rule-based or statistical methods, they still require a lot of manual effort for feature engineering and do not handle the intricacies of language (like context, idioms, and ambiguities) very well.