Now that you have implemented the SearchEngine, we want to extend the search function to support multi-word queries. Note that you should only need to change the search function of your SearchEngine class implemented in Part 1.
To compute the TF-IDF of a multi-word query, we compute the TF-IDF for each individual term in the query and then sum these terms to compute the total TF-IDF.
For example, if the query was “a cute dog”, the TF-IDF of the query for document D would be
Similar to part 1, we compute the TF-IDF for each document that contains a term in the query and sort the documents by descending order of TF-IDF.
However, finding the relevant documents for a multi-word query is a bit more challenging. Instead of looking at a single entry in the dictionary, we must look at all Documents which contain at least one word in the query. This means if the query is “a cute dog” the TF-IDF should be computed for all documents in the SearchEngine that contain the word “a”, all documents that contain the word “cute”, and all documents that contain the word “dog”. Even if a document only contains the word “a”, it should still be included in the ranking - its ranking will just be lower because its TF-IDF score for “cute” and “dog” will be 0.