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Intention based information retrieval

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Information about Intention based information retrieval
Technology

Published on March 7, 2009

Author: atripathy86

Source: slideshare.net

Description

This poster shows the idea behind Intention based Information Retrieval. Developed primarily by Amitava Biswas, Suneil Mohan, Jagannath Panigrahy, Aalap Tripathy and Prof Rabi Mahapatra at the Embedded Systems Codesign Group at Texas A&M Uivversity, College Station, TX, USA
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Meaning representation for Intention based search Aalap Tripathy , Amitava Biswas, Suneil Mohan, Jagannath Panigrahy & Rabi Mahapatra Embedded Systems & Co-design Lab, Texas A&M University Intention based search Meaning Representation “Precise identification of the best available information that “The fisherman wearing a green matches user’s search intention” Text shirt, caught a big trout” Representation of Meaning Concept Tree Example of a search intention 0.77 0.4 0.45 Search for “publication on role of 1858C gene variant in catch Concept Tree 0.38 diabetes in North American population” 0.5 0.3 0.4 representation fisherman big trout 0.6 0.7 A green shirt The Problem C B Computers have limited ability to interpret meaning from human generated content (e.g. text) 0.5 × A>BC + 0.5 × BC< A + 0.3 × A>C B + 0.4 × C B< A Keyword based search can not discern: Tensor + 0.3 × A>B + 0.3 × B< A + 0.1 × A>C + 0.1 × C < A “It was not the sales manager who hit the representation bottle that day, but the office worker with + 0.4 × BC + 0.1 × C B + 0.1 × A + 0.1 × B + 0.1 × C the serious drinking problem.” from Basis Vectors Terms “That day the office manager, who was drinking, hit the problem sales worker with = “fisherman >greenshirt” A> B = “fisherman > green” A> B C a bottle, but it was not serious.” (Mitchell et al., ACL, 2008) = greenshirt < fisherman” B< A = “green < fisherman” BC > A C B = “shirtgreen” = “greenshirt” BC Key Challenge How to represent and compare meaning in computers? Meaning Similarity Comparison Similarity Computation as a dot product of two tensors Search & Retrieval Process Object Key Tensor T1 = 0.38Y Z + 0.5Z Y + 0.1 X + 0.1Y + 0.1Z + ... User makes Object Collection search query “Q” Search Key Tensor T2 = 0 . 91 Y Z + 0 . 41 X Key “K” Object “O” Similarity (T1,T2) = T1 • T 2 K1 O1 K5 O5 Common Basis Vectors (CBV) between T1 and T2 : Y Z, X K7 O7 K2 O2 T1 • T 2 = 0 . 38 × 0 . 91 + 0 . 1 × 0 . 41 = 0 . 38 K6 O6 K3 O3 K9 O9 Search Key “Q” To pair up matching coefficients of T1 & T2, we use Bloom Filters K8 O8 K4 O4 Similarity Computation using Bloom Filters (BF) Steps for searching 1. Tensor is encoded in a Bloom Filter and a Coefficient Table 2. Object Key BF (T1) & Search Key BF (T2) are BITwise ANDed 1. The meaning is represented in the form of a concept tree 3. Coefficients are extracted from table and pairwise multiplied & summed 2. The concept tree is used as a search key 3. Intention (search) key is compared with all object keys Basis Vector X BF of BF of BF of 4. Objects with matching keys are retrieved Object Key (T1) Coefficient Table CBV T2 T1 F1(X)= 1 Hash(X) Coefficients BF Indices 1 1 1 B12A34 0.38 {1,3,7,…} 0 0 0 F2E123 0.5 {3,8,10,...} F2(X)= 3 Experimental Results 0 1 0 E2E424 0.1 {1,5,6,…} 1 0 0 56EF78 0.1 {6,7,8,…} Tensor model represents meaning better than : BITwise 1 1 1 .. … … TF-IDF model in 95% cases & AND 1 1 1 ontology based vector model in 92% cases. Fj(X)= k 1 1 1 Search Key (T2) Coefficient Table Contributions 0 1 0 Hash(X) Coefficients BF Indices Technique to represent meaning in computers 0 1 0 B12A34 0.91 {1,3,7,…} 1 1 1 E2E424 0.41 {1,5,6,…} Comparison of meaning now possible

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