A company has documents that are missing some words because of a database error. Which type of model meets this requirement? A. Topic modeling B. Clustering models C. Prescriptive ML models D. BERT-based models
Answer (1)
To address the issue of a company having documents with missing words due to a database error, a model that understands and generates natural language effectively would be most suitable. The options presented are:
A. Topic modeling: This is used to identify topics within a set of documents. It is not specifically designed to fill in missing words.
B. Clustering models: These are used for grouping similar data points but do not address filling in missing text in documents.
C. Prescriptive ML models: These models provide recommendations based on past data but are not specifically designed for reconstructing or filling in text.
D. BERT-based models: BERT (Bidirectional Encoder Representations from Transformers) is a popular natural language processing model developed by Google. It understands the context of words in a sentence and can predict or fill in missing words in a given text with high accuracy.
Given the requirements, D. BERT-based models would be the best choice. These models are specifically engineered to comprehend natural language profoundly and handle tasks like question answering, sentence completion, and text prediction, which makes them ideal for filling in missing words in documents.
