Which solution meets these requirements?
Extract the topics from each article by using Latent Dirichlet Allocation (LDA) topic modeling. Create a topic table by assigning the sum of the topic counts as a score for each word in the articles. Configure the tool to retrieve the articles where this topic count score is higher for the queried words.
Build a term frequency for each word in the articles that is weighted with the article’s length. Build an inverse document frequency for each word that is weighted with all articles in the corpus. Define a final highlight score as the product of both of these frequencies. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.
Download a pretrained word-embedding lookup table. Create a titles-embedding table by averaging the title’s word embedding for each article in the corpus. Define a highlight score for each word as inversely proportional to the distance between its embedding and the title embedding. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.
Build a term frequency score table for each word in each article of the corpus. Assign a score of zero to all stop words. For any other words, assign a score as the word’s frequency in the article. Configure the tool to retrieve the articles where this frequency score is higher for the queried words.
Explanations:
While LDA can extract topics from articles, it may not effectively isolate the most frequently used or important words for specific queries. Topic modeling focuses on overall themes rather than individual word relevance, which does not meet the requirement of isolating important words in documents.
This option employs term frequency (TF) and inverse document frequency (IDF), which are standard techniques for measuring the importance of words in documents. By calculating a highlight score based on these metrics, the tool can effectively retrieve articles where the queried words are most relevant and significant, aligning with the editors’ needs.
Using a pretrained word-embedding lookup table focuses on semantic similarity rather than word frequency or relevance. While it could capture the meaning of words, it does not directly address the frequency or importance of specific words in the articles, which is essential for the search tool’s goal.
This option disregards the length of articles, which could lead to misleading scores, particularly for longer documents. Assigning a score of zero to stop words does not account for the importance of non-stop words across varying contexts, limiting the ability to isolate relevant articles for specific queries.
I strategize that the answer is:
Build a term frequency for each word in the articles that is weighted with the article’s length. Build an inverse document frequency for each word that is weighted with all articles in the corpus. Define a final highlight score as the product of both of these frequencies. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.