A news company is developing an article search tool for its editors. The search tool should look for the articles that are most relevant and representative for particular words that are queried…

Latent Dirichlet Allocation (LDA) is a topic modeling technique used to discover abstract topics in a collection of documents, but it doesn't directly provide word importance scores for specific queries in the same way TF-IDF does.

Building a Term Frequency-Inverse Document Frequency (TF-IDF) score for each word in each article directly addresses the requirements. TF-IDF measures the importance of a word in a document relative to a corpus by weighting its frequency within the document (Term Frequency) against its inverse frequency across all documents (Inverse Document Frequency), thus isolating important and relevant words.

Word embeddings capture semantic relationships between words and are useful for similarity, but they don't inherently quantify the 'relevance and importance' of a word within a specific document relative to a corpus for keyword search in the manner TF-IDF does.

Building a simple term frequency table or assigning a score of 1 to each word only captures presence or raw frequency, which is what the editors found insufficient, as it doesn't account for the importance or rarity of words across the entire document corpus.