Bigram

See also: Machine learning terms

Bigram in Machine Learning

A bigram is a fundamental concept in the field of natural language processing (NLP), a subfield of machine learning. Bigrams are pairs of consecutive words in a given text or sequence of words. They play a vital role in various NLP tasks, such as language modeling, text classification, and sentiment analysis, by capturing the contextual information of words in a language.

Definition and Notation

Formally, a bigram is a tuple of two consecutive words (w1, w2) in a text, where w1 and w2 are words from a given vocabulary. Bigrams can be represented as a matrix, where the rows and columns correspond to the words in the vocabulary, and the matrix cell at position (i, j) contains the frequency or probability of the corresponding word pair. This matrix is often referred to as a bigram probability matrix.

In mathematical notation, a bigram probability can be expressed as P(w2|w1), which denotes the probability of observing word w2 after word w1 in a given text. The probabilities can be estimated using Maximum Likelihood Estimation (MLE) or other smoothing techniques, such as Laplace smoothing or Kneser-Ney smoothing.

Applications of Bigrams in NLP

Bigrams are commonly used in a variety of NLP tasks to capture the contextual relationships between words. Some notable applications include:

• Language Modeling: Bigrams are employed in n-gram language models to predict the probability of a word occurring in a given context. These models can be used for tasks such as speech recognition, machine translation, and text generation.
• Text Classification: Bigrams can be utilized as features in text classification tasks to better capture the word dependencies in the text. For example, in sentiment analysis, the presence of specific bigrams can be indicative of positive or negative sentiment.
• Spell Checking and Correction: Bigram probabilities can be used to assess the likelihood of a given word sequence in the context of a language model. This information can be valuable for identifying and correcting spelling errors or typos.

Explain Like I'm 5 (ELI5)

Imagine you have a bunch of toy blocks, each with a different word on it. A bigram is just two of these blocks placed side by side, making a pair of words. In the world of computers and language, bigrams help us understand how often two words are found together. This information is useful for tasks like guessing what word comes next, figuring out if a sentence is happy or sad, or fixing spelling mistakes.

Introduction

In the field of machine learning and natural language processing (NLP), a bigram is a fundamental concept that refers to a pair of consecutive words or tokens in a given sequence of text. Bigrams are used in various NLP and machine learning tasks, such as language modeling, feature extraction, and pattern recognition, among others. This article will discuss the significance of bigrams in machine learning, their applications, and how they contribute to enhancing the performance of different algorithms.

Bigram Representation

A bigram is created by dividing a text sequence into consecutive pairs of words or tokens. The primary goal of using bigrams is to capture contextual relationships between adjacent words, which can be helpful in improving the performance of various machine learning models.

Tokenization

Before forming bigrams, the raw text must be preprocessed and tokenized. Tokenization is the process of breaking down a text into individual words or tokens. This is typically done using various NLP techniques, such as whitespace tokenization, rule-based tokenization, or more advanced methods like stemming and lemmatization.

Formation of Bigrams

Once the text is tokenized, bigrams can be generated by considering every consecutive pair of words or tokens. For example, given the sentence "The cat sat on the mat," the bigrams generated would be: ("The", "cat"), ("cat", "sat"), ("sat", "on"), ("on", "the"), and ("the", "mat").

Applications of Bigrams

Bigrams are widely used in numerous machine learning and NLP tasks, including but not limited to:

Language Modeling

In language modeling, bigrams are used to estimate the probability of a word occurring, given its preceding word. This helps to capture the local context in a sentence and results in more accurate language models compared to unigram models, which consider words in isolation.

Text Classification

Bigrams can serve as valuable features for text classification tasks, where the goal is to categorize documents into predefined classes. By incorporating bigram features, classification algorithms can leverage the contextual information present in the text, leading to better performance.

Information Retrieval

In information retrieval, bigrams can improve search engine performance by considering the co-occurrence of words in documents. This helps to identify documents that are more relevant to a given query, as it takes into account the relationship between adjacent words.

Spell Checking and Correction

Bigrams can also be employed in spell checking and correction systems, where they aid in identifying and suggesting corrections for misspelled words based on the surrounding context.

Explain Like I'm 5 (ELI5)

A bigram is simply two words that appear together in a sentence or text. Imagine you have a bunch of words written on small pieces of paper, and you want to find out which words often appear side by side. You could look at every pair of neighboring words and start counting how many times they show up together. This is what a bigram does! Bigrams help computers understand the context and meaning of words in a sentence, which can be useful for tasks like predicting what word comes next, figuring out what a piece of writing is about, or finding mistakes in spelling.