Lexical Knowledge Structures

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GOOSE : working [3] Parses the query into semantic frame. Classify into common sense sub-domain. Reformulation Apply reasoning using inference chain. Heuristically guided. Termination on application-level rule. extract the reformulated search term. Search on commercial search engine. Re-ranking Based on weighted concepts. A K Nirala Lexical Knowledge Structures
GOOSE : a scenario [3] Goal : I want help solving this problem and query, my golden retriever has a cough Problem Attribute [cough] Parsing gives Problem Object [golden retriever] commonsense sub-domain classified : animals with the chain A golden retriever is a kind of dog. A dog may be a kind of pet. Something that coughs indicates it is sick. Veterinarians can solve problems with pets that are sick. Veterinarians are locally located. The reformulated search is Veterinarians, Cambridge MA Location obtained from user profile. Page containing concepts closer to veterinarians is ranked high A K Nirala Lexical Knowledge Structures
Page 1 and 2: Lexical Knowledge Structures By Ash
Page 3 and 4: SHRDLU (1971) A K Nirala Lexical Kn
Page 5 and 6: SHRDLU : a success story. Considere
Page 7 and 8: ConceptNet ConceptNet A K Nirala Le
Page 9 and 10: Typical relations in concept net A
Page 11 and 12: Relations in ConceptNet Agents, Thi
Page 13 and 14: Relations in ConceptNet Functional,
Page 15 and 16: Development Process of ConceptNet v
Page 17 and 18: Extraction phase Relations are extr
Page 19 and 20: Relaxation phase (contd.) SuperThem
Page 21 and 22: Evaluation of accumulated data 8 ju
Page 23 and 24: Knowledge acquisition from the gene
Page 25 and 26: OMCS web interface A sample web int
Page 27 and 28: Feedback and Inference (contd.) Met
Page 29 and 30: More user inputs Clarification by s
Page 31 and 32: ConceptNet5 ConceptNet5 released on
Page 33 and 34: URI hierarchy Uniform Resource Iden
Page 35 and 36: Concept URIs Each concept has minim
Page 37 and 38: ConceptNet5.1 WEB API Lookup : When
Page 39 and 40: API for Association BASE URL : http
Page 41: GOOSE 2004 Goal-Oriented Search Eng
Page 45 and 46: Other applications [4] Commonsense
Page 47 and 48: Information Extraction Google searc
Page 49 and 50: YAGO data model, few examples Elvis
Page 51 and 52: YAGO Model: Formal view common enti
Page 53 and 54: Classes for all literals Classes fo
Page 55 and 56: Semantics : Rewrite rule (contd.)
Page 57 and 58: Semantics : Rewrite rule (contd) Gi
Page 59 and 60: Theorems & Corollary Given F = (I
Page 61 and 62: Sources for YAGO Sources and Inform
Page 63 and 64: Information Extraction Two steps (Y
Page 65 and 66: Infoboxes Infobox type establishes
Page 67 and 68: Types of facts Category system of W
Page 69 and 70: Defining hierarchy of classes using
Page 71 and 72: Word heuristics A means relation is
Page 73 and 74: Quality Control & Type Checking Can
Page 75 and 76: Evaluating YAGO Randomly selected f
Page 77 and 78: YAGO 2 : Extensible Extraction Arch
Page 79 and 80: Information Extraction from differe
Page 81 and 82: YAGO in development of ontologies Y
Page 83 and 84: Downloading YAGO Freely available a
Page 85 and 86: VerbOcean Developed at University o
Page 87 and 88: Relations captured by VerbOcean Sim
Page 89 and 90: Relations captured by VerbOcean Ena
Page 91 and 92: Extracting Associated verb pairs 1.
Page 93 and 94:
Lexico-syntactic patterns (contd.)
Page 95 and 96:
Lexico-syntactic patterns (contd.)
Page 97 and 98:
Scoring the verb pair on the patter
Page 99 and 100:
Pruning If the pattern matching was
Page 101 and 102:
Bibliography I Timothy Chklovski an
Page 103 and 104:
Bibliography III Mueller E T Lim G
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Lexical Knowledge Structures

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