Core Techniques in Natural Language Processing (NLP) with Examples

Core Techniques in Natural Language Processing (NLP) are used to process and analyze text data. This article gets into core techniques such as Bag of Words (BoW), TF-IDF, word embeddings (Word2Vec, GloVe, FastText), and sequence models (RNNs, LSTMs, GRUs) providing concise examples to demonstrate their practical application.

Bag of Words (BoW) and TF-IDF

Explanation of BoW and Its Limitations

The Bag of Words (BoW) model represents text by transforming it into a collection of word occurrences or frequencies. It ignores grammar and word order, focusing solely on the frequency of each word in the text.

Example:

Consider two simple sentences:

1. “I love NLP.”
2. “NLP is great.”

Limitations:

1. Context Ignorance: Does not capture the meaning or context of words.
2. Dimensionality: Large vocabulary results in high-dimensional vectors.
3. Word Order: Disregards the order of words.

Introduction to TF-IDF and Its Importance in Text Representation

Term Frequency-Inverse Document Frequency (TF-IDF) improves upon BoW by weighing words based on their importance in the document and across the corpus.

Example:

Using the same sentences, calculate TF-IDF:

Here, common words like “is” have lower weights and unique words like “love” and “great” have higher weights, highlighting their importance.

Word Embeddings

Word2Vec: Skip-gram and Continuous Bag of Words (CBOW)

Word2Vec generates dense vector representations of words, capturing semantic relationships. It has two architectures: SkiWord2Vec is a neural network-based model that generates dense vector representations of words, capturing semantic relationships. It has two architectures:

Skip-gram: Predicts surrounding words given a central word. It excels in capturing the context of infrequent words.
CBOW (Continuous Bag of Words): Predicts a central word from a window of surrounding context words. It is more efficient for large datasets and common words.

Example:

For simplicity, consider the sentence “I love NLP”:

Skip-gram: Given “love”, predict “I” and “NLP”.

CBOW: Given “I” and “NLP”, predict “love”.

Using a small corpus, Word2Vec learns that similar words have closer vector representations.

GloVe (Global Vectors for Word Representation)

GloVe produces word vectors by leveraging word co-occurrence statistics from a large corpus. It captures both global and local context.

Using a simple corpus, GloVe might produce vectors where the cosine similarity between “king” and “queen” is similar to the cosine similarity between “man” and “woman”, reflecting their semantic relationships.

FastText: Subword Information

FastText extends Word2Vec by considering subword information, making it effective for morphologically rich languages.

Example:

For the word “unhappiness”, FastText considers subwords like “un”, “happy”, and “ness”. This allows it to generate embeddings for out-of-vocabulary words by combining subword embeddings.

Sequence Models

Introduction to Recurrent Neural Networks (RNNs)

RNNs handle sequential data by maintaining a hidden state that captures information from previous time steps.

Example:

Consider the sentence “I love Machine Learning”. An RNN processes each word sequentially, updating its hidden state to remember the context.

Long Short-Term Memory (LSTM) Networks

LSTMs address RNN limitations by using memory cells to maintain long-term dependencies.

Example:

For the sentence “The cat, which was small and cute, sat on the mat”, an LSTM can remember the subject “cat” across the long sequence, effectively capturing long-term dependencies.

Gated Recurrent Units (GRUs)

GRUs simplify LSTMs by combining the forget and input gates into a single update gate, reducing computational complexity.

Example:

Using the same sentence, “The cat, which was small and cute, sat on the mat”, a GRU efficiently maintains the context of “cat” without the complexity of LSTMs.

Conclusion

Grasping and implementing these fundamental techniques— BoW, TF-IDF, word embeddings, and sequence models—are essential for creating robust NLP applications. Each method offers unique strengths and can be applied to various tasks, from text classification to language generation. By mastering these techniques with small examples, you can create models that understand and generate human language with remarkable accuracy.