Lexical Knowledge Structures

More documents

Recommendations

Info

Feedback and Inference Method 1 : Analogies over concept Slots filled in the template are searched for other templates. A mother can have a baby gives A mother can hold her baby Then other relations matching this newly found template are searched A small girl can hold her small dog For each match, slots values are replaced with the found one. A small girl can have a small dog If user finds it correct he may confirm this. A K Nirala Lexical Knowledge Structures
Feedback and Inference (contd.) Method 2 : Analogies over Relations Template are searched for other concepts. A mother can have a baby gives A child can have a goldfish Then for new slots values other Template are searched. A child can take care of goldfish For each match, slots values are replaced with entered one. A mother can take care of a child If user finds it correct he may confirm this. 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: OMCS web interface A sample web int
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 and 42: GOOSE 2004 Goal-Oriented Search Eng
Page 43 and 44: GOOSE : a scenario [3] Goal : I wan
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
show all

Lexical Knowledge Structures

Create successful ePaper yourself

Delete template?

Save as template?