Select Lexis Tax—Federal Topical database. Perform a natural-language search to determine the tax treatment of stock redeemed from a deceased shareholder. Perform the same search using terms and connectors. List your natural-language search and the number of documents it retrieved. Provide the same information for the terms and connectors search. Explain why one was a more effective search.
Added by Tina M.
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- Search Query: "tax treatment of stock redeemed from a deceased shareholder" - Number of Documents Retrieved: [Insert number of documents retrieved here] Show more…
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Suppose we have the query "oil producing nations", and the three query terms have inverted lists as follows: Ik → (dfk, ctfk, (doci, tfik), ...) oil → (5, 18, (1, 4), (4, 3), (6, 1), (7, 2), (8, 8)) producing → (4, 20, (1, 6), (2, 2), (5, 4), (8, 8)) nations → (3, 11, (1, 1), (3, 2), (8, 8)) Further suppose we have a collection of documents with lengths as follows: d1 → 498 d6 → 639 d2 → 627 d7 → 566 d3 → 551 d8 → 423 d4 → 648 d9 → 589 d5 → 621 d10 → 525 and the total number of terms in the corpus is 5687. What are the scores of the 10 documents using each of the following retrieval models: (a) Vector space model with term weighting: (b) Binary independence model (c) BM25 model with parameters k1 = 1.2, b = 0.75, k3 = 0 (d) Language model with Jelinek-Mercer smoothing parameter λ = 0.2 (e) Language model with Dirichlet smoothing parameter µ = 2000 Explain what makes these models different from each other, focusing especially on how they use term frequency, document frequency, and document length to calculate document scores.
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