Compare and contrast the bigram association measures. Suggest suitable scenario for their use.

Bigram association measures are statistical metrics used to quantify the association or relationship between pairs of consecutive words (bigrams) in a corpus or text dataset. They help identify significant and meaningful associations between words, which can be useful in various natural language processing (NLP) tasks, such as text classification, information retrieval, and language modeling. Here are some commonly used bigram association measures and their characteristics:

1. Pointwise Mutual Information (PMI):

   • PMI measures the degree of association by comparing the observed frequency of a bigram with the expected frequency under independence.

   • It calculates the log-ratio of the joint probability of the bigram to the product of the individual word probabilities.

   • PMI can capture both positive and negative associations, and higher values indicate stronger associations.

   • Suitable Scenario: PMI is widely used in applications like keyword extraction, sentiment analysis, and collocation detection. It is suitable when you need to identify statistically significant word associations in a given corpus.

2. Mutual Information (MI):

   • MI measures the amount of information that two words share by comparing their joint probability with their individual probabilities.

   • It calculates the difference between the joint probability and the product of the individual word probabilities.

   • MI can capture both positive and negative associations, and higher values indicate stronger associations.

   • Suitable Scenario: MI is commonly used in information retrieval tasks, such as document retrieval and query expansion, where identifying word associations helps improve search accuracy.

3. Log-Likelihood Ratio (LLR):

   • LLR measures the likelihood of the observed bigram frequency compared to the expected frequency under a null hypothesis of independence.

   • It calculates the log-ratio of the observed frequency to the expected frequency.

   • LLR can capture both positive and negative associations, and higher values indicate stronger associations.

   • Suitable Scenario: LLR is often used in text classification tasks, such as spam detection and sentiment analysis, where identifying significant word associations can improve classification accuracy.

4. Chi-square (χ²) Test:

   • The chi-square test measures the difference between the observed frequency and the expected frequency of a bigram under the null hypothesis of independence.

   • It calculates the chi-square statistic, which indicates the deviation from independence.

   • Chi-square can capture both positive and negative associations, and higher values indicate stronger associations.

   • Suitable Scenario: The chi-square test is commonly used in feature selection for text classification and information retrieval tasks. It helps identify informative bigrams that are more likely to be associated with specific classes or topics.

When choosing a suitable bigram association measure, consider the specific requirements of your NLP task and the nature of the data. PMI, MI, LLR, and chi-square are all popular measures, but their suitability depends on the particular application and the desired interpretation of word associations.