Lexical Knowledge Structures

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SHRDLU, Demo by Terry Winograd at the MIT AI Lab (1971) The dialog that was used as a SHRDLU demo (in 1971): 1 1 taken from : http://hci.stanford.edu/winograd/shrdlu/index.html A K Nirala Lexical Knowledge Structures
SHRDLU : a success story. Considered a significant step forward in NLP, as it combines models of human linguistic reasoning methods in the language understanding process. But so far has not been extended further. Works in simple, logical, and closed domain. Can-not handle hypothesis. Things are totaly abstracted. A K Nirala Lexical Knowledge Structures
Page 1 and 2: Lexical Knowledge Structures By Ash
Page 3: SHRDLU (1971) A K Nirala Lexical Kn
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 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
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Semantics : Rewrite rule (contd.)
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Semantics : Rewrite rule (contd) Gi
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Theorems & Corollary Given F = (I
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Sources for YAGO Sources and Inform
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Information Extraction Two steps (Y
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Infoboxes Infobox type establishes
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Types of facts Category system of W
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Defining hierarchy of classes using
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Word heuristics A means relation is
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Quality Control & Type Checking Can
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Evaluating YAGO Randomly selected f
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YAGO 2 : Extensible Extraction Arch
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Information Extraction from differe
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YAGO in development of ontologies Y
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Downloading YAGO Freely available a
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VerbOcean Developed at University o
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Relations captured by VerbOcean Sim
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Relations captured by VerbOcean Ena
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Extracting Associated verb pairs 1.
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Lexico-syntactic patterns (contd.)
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Lexico-syntactic patterns (contd.)
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Scoring the verb pair on the patter
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Pruning If the pattern matching was
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Bibliography I Timothy Chklovski an
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Bibliography III Mueller E T Lim G
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