Description

Orthogonal Sparse Bigram (OSB) is a text processing technique used in Natural Language Processing (NLP) and Information Retrieval (IR). It’s designed to capture the context of words in a document by considering not only individual words but also pairs of words that appear within a certain distance from each other.

How OSB Works

OSB works by applying a fixed-size sliding window over the text. For each position of the window, it generates a set of bigrams consisting of the word at the center of the window and each of the other words in the window. These bigrams are treated as separate features in the resulting feature vector.

Benefits

• Context Capture: OSB captures more context than simple bag-of-words models.
• Dimensionality Reduction: OSB can reduce the dimensionality of the feature space compared to full bigram models.
• Improved Performance: OSB often leads to improved performance in text classification tasks.

Limitations

• Increased Complexity: OSB increases the complexity of the feature extraction process.
• Parameter Sensitivity: The performance of OSB can be sensitive to the choice of window size.

Features

• Bigram Generation: OSB generates bigrams based on a sliding window.
• Orthogonality: OSB treats each bigram as a separate, orthogonal feature.
• Sparse Representation: OSB results in a sparse feature vector, where each element corresponds to a specific bigram.

Use Cases

• Text Classification: OSB can be used to extract features for text classification tasks.
• Information Retrieval: OSB can be used in IR systems to improve the retrieval of relevant documents.
• Sentiment Analysis: OSB can be used in sentiment analysis to capture more context than individual words.