5) How would you construct a term-context matrix. How would you use it?
To construct a term-context matrix, you follow these steps:
1. Define the terms: Determine the terms (words or phrases) you want to analyze in your text data. These terms are usually referred to as the target terms.
2. Define the context: Determine the context words around the target terms that you want to consider. The context can be defined by a window size, which represents the number of words before and after the target term.
3. Tokenize the text: Tokenize the text into individual words or phrases. This can be done by splitting the text on whitespace or using more advanced natural language processing techniques.
4. Iterate through the text: For each target term, iterate through the text and identify the context words within the defined window size.
5. Increment counts: Increment the count of occurrences whenever a target term and context word pair is found.
6. Create the matrix: Create a matrix with rows representing the target terms and columns representing the context words. The matrix entries contain the counts of how many times a specific context word occurs in the context of a specific target term.
To use the term-context matrix, you can perform various analyses, such as:
1. Word similarity: Analyze the similarity between different terms based on their context. Terms that have similar context words are likely to have similar meanings or usage.
2. Topic modeling: Apply topic modeling techniques, such as Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), to extract latent topics from the matrix.
3. Information retrieval: Use the matrix to retrieve relevant documents when given a target term or a context word.
4. Prediction: Train machine learning models using the term-context matrix as input features to predict various outcomes, such as sentiment analysis or document classification.
Overall, the term-context matrix helps in understanding how terms are related to their context and enables various NLP analyses and applications.