From N-grams to Word2Vec: The Early Steps in Language Representation

Introduction

Language representation in computational models is a central topic in natural language processing (NLP). In its early stages, statistical methods like n-grams were the norm. However, the need to better understand context and semantic relationships led to the development of more advanced techniques such as Word2Vec. In this article, we’ll explore the evolution of language representation approaches, from n-grams to word embeddings, learn how to implement them in Python, and look at real-world application examples.

N-grams: An Introduction

What Are N-grams?

An n-gram is a contiguous sequence of "n" elements (words or characters) in a text. For example, given the sentence:

"Artificial intelligence is fascinating."

N-grams are generated as follows:

Unigrams (n=1): ["Artificial", "intelligence", "is", "fascinating"]
Bigrams (n=2): [("Artificial", "intelligence"), ("intelligence", "is"), ("is", "fascinating")]
Trigrams (n=3): [("Artificial", "intelligence", "is"), ("intelligence", "is", "fascinating")]

N-grams are useful in tasks such as text prediction but face significant limitations, especially for capturing broader contexts and complex semantic relationships.

Real-world Use Cases for N-grams

Text Prediction: N-grams are used in predictive keyboards to suggest words based on likely patterns.
Spam Filtering: They help identify common word combinations in spam emails.
Frequency Analysis: Used in linguistic data analysis to study recurring patterns in different languages.

Advantages and Limitations

Advantages:
- Simple to implement.
- Capture basic local patterns in text.
Limitations:
- Exponential Growth: The number of possible combinations grows rapidly with "n."
- Lack of Semantics: They fail to capture meaning or relationships beyond their immediate neighborhood.
- Sparsity: Many possible combinations are absent in the corpus.

Practical Implementation

from nltk import ngrams  
from collections import Counter  
 
# Example text  
text = "Artificial intelligence is fascinating".split()  
 
# Generate bigrams  
bigrams = list(ngrams(text, 2))  
print("Bigrams:", bigrams)  
 
# Count the frequency of the bigrams  
frequency = Counter(bigrams)  
print("Bigram frequency:", frequency)

Expected output:

Bigrams: [('Artificial', 'intelligence'), ('intelligence', 'is'), ('is', 'fascinating')]  
Bigram frequency: Counter({('Artificial', 'intelligence'): 1, ('intelligence', 'is'): 1, ('is', 'fascinating'): 1})

The Conceptual Leap: From N-grams to Word2Vec

What Is Word2Vec?

Word2Vec, introduced by Google in 2013, represents a significant step forward in understanding the semantic and syntactic relationships between words. Unlike n-grams, which rely on fixed-sized word sequences, Word2Vec leverages a neural network to encode words into dense, continuous vectors in a high-dimensional space. This approach enables it to capture deeper contextual meanings and relationships between words.

Word2Vec is built on the distributional hypothesis, which states that words that appear in similar contexts tend to have similar meanings. It provides two core architectures:

Continuous Bag of Words (CBOW): Predicts a target word based on its surrounding context words.
Skip-gram: Predicts surrounding context words given a target word.

These methods make Word2Vec particularly effective in learning representations that reflect the similarity and relationship between words in a meaningful way.

Real-world Use Cases for Word2Vec

Search Engines: Enhance query understanding and relevance by identifying semantic connections between search terms and content.
Customer Sentiment Analysis: Classify user reviews or feedback as positive, negative, or neutral by analyzing the relationships between opinion words.
Machine Translation: Map words from different languages to a shared semantic space, improving translation accuracy.
Recommendation Systems: Power recommendation engines (e.g., Amazon, Netflix) by associating related items based on user reviews and descriptions.
Biological Sequence Analysis: Apply word embeddings to DNA, RNA, or protein sequences, treating biological sequences as "words" for pattern detection.
Social Media Insights: Track trends and sentiment shifts by embedding text from tweets or posts.

Strengths and Weaknesses of Word2Vec

Strengths:

Dense Representations: Encodes meaning in compact, dense vectors, reducing the sparsity typical in earlier methods.
Semantic and Syntactic Learning: Captures relationships like similarity (e.g., king and queen) and analogies (e.g., Paris is to France as Berlin is to Germany).
Scalability: Efficiently handles large text datasets and computes embeddings for extensive vocabularies.

Weaknesses:

Context Limitations: Word2Vec does not account for the full sentence or paragraph context, as it considers only local neighborhoods.
Dependency on Data Volume: Requires a substantial amount of diverse text data to produce high-quality embeddings.
Static Representations: Fails to capture multiple meanings of polysemous words (e.g., "bank" as a financial institution vs. a riverbank).

Basic Implementation with Gensim

To understand how Word2Vec works, let’s implement a basic version using the Gensim library.

from gensim.models import Word2Vec  
from nltk.tokenize import word_tokenize  
import nltk  
nltk.download('punkt')  
 
# Example corpus  
text = [  
    "Artificial intelligence is fascinating",  
    "Intelligence is key to solving problems",  
    "AI is transforming the world"  
]  
 
# Tokenize the text  
corpus = [word_tokenize(sentence.lower()) for sentence in text]  
 
# Train the Word2Vec model  
model = Word2Vec(corpus, vector_size=50, window=3, min_count=1, sg=1)  
 
# Get the vector for a word  
word_vector = model.wv['intelligence']  
print("Vector for 'intelligence':", word_vector)  
 
# Find similar words  
similar_words = model.wv.most_similar('intelligence', topn=5)  
print("Words similar to 'intelligence':", similar_words)

Expected output:

Vector for 'intelligence': [0.123, -0.235, ...]  # (Valores de ejemplo)  
Words similar to 'intelligence': [('artificial', 0.89), ('key', 0.75), ...]

Conclusion

The transition from methods like n-grams to Word2Vec marked a milestone in natural language processing. While n-grams were useful for simple tasks and local patterns, Word2Vec paved the way for semantic representation, enabling more advanced applications such as sentiment analysis, machine translation, and text generation.

Understanding these foundations is crucial for exploring modern models like BERT and GPT, which take these ideas to new heights. The future of NLP is bright, and Word2Vec remains a cornerstone in this exciting field!