the table:

2. Indexing Models and Term Weighting Consider the following document-term table containing raw term frequencies. Answer the following questions, and in each case give the formulas you used to perform the necessary computations. Note: you should not do these computations manually. You may use a spreadsheet program such as Microsoft Excel, or you can considering writing your own program do the computations. In either case, include your spreadsheet or code in your assignment submissions. Download the table below as an Excel Spreadsheet] Terml Term2 Term3 Term4 Terms Termo Term7 Term8 0 0 2 1 @ 300 3 4 0 0 Hin DOC1 0 3 1 DOC2 53 DOC3 3 0 4 DOC4 1 DOC5 @ DOC6 2 DOCZ 250 DOC8 3 3 0 DOC9 0 0 3 DOC10 1 0 5 2 WNANO ++ oHo 3 0 1 2 0 0 3 3 0 0 0 24 0 2. a. Compute the new weights for all the terms in document DOC4 using the if x idf approach. b. Compute the new weights for all the terms in documents DOC4 using the signal-to-noise ratio approach. c. Using the Keyword Discrimination approach, determine if Term4 is a good index term or not (by computing it's discriminant). To compute average similarities use Cosine similarity as your similarity measure. Show your work. A I C Term2 D Term3 E Term4 F Term5 G Term6 H Term7 Term8 2 B Term1 0 5 3 1 0 0 0 8 4 0 4 0 DOC1 DOC2 DOC3 DOC4 DOC5 DOC6 DOC7 DOC8 DOC9 DOC10 0 2 mmomno 10 3 0 1 11 12