Unsupervised Ontology Induction From Text - Microsoft Research

15.01.2014 • Views

Share Embed Flag

Unsupervised Ontology Induction From Text - Microsoft Research

Unsupervised Ontology Induction From Text - Microsoft Research

DOWNLOAD ePAPER

research.microsoft.com

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

START NOW

OntoUSP<br />

• USP + Hierarchical clustering + Shrinkage<br />

• Modify the cluster mixture formula<br />

InClust(e,c) ^ ISA(c,+d) ^ HasValue(e,+v)

1
2
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

PDF file - Microsoft Research

Conversations in the Crowd - Microsoft Research

PDF file - Microsoft Research

Wearing the hair shirt Wearing the hair shirt - Microsoft Research

Probabilistic Relational Verification for Cryptographic Implementations

NLP for IR - Microsoft Research

Mobile Computing Research Is a Hornet's Nest of Deception and ...

Solution Set #1 - Microsoft Research

slides - Microsoft Research

PDF file - Microsoft Research

Hierarchical Probabilistic Segmentation of Discrete Events

POSH: A TLS Compiler that Exploits Program Structure∗ - Microsoft ...

PDF file - Microsoft Research

Trusted Computing-Based Security Architecture For 4G Mobile ...

Outline • Background: USP • <strong>Unsupervised</strong> ontology induction • Conclusion 24

OntoUSP • USP + Hierarchical clustering + Shrinkage • Modify the cluster mixture formula InClust(e,c) ^ ISA(c,+d) ^ HasValue(e,+v)

Page 1 and 2: Unsupervised Ontology Induction Fro
Page 5 and 6: Extracting Knowledge From Text …
Page 7 and 8: Unsupervised Learning for Knowledge
Page 9 and 10: Ontology Learning • Attracts incr
Page 11 and 12: Outline • Background: USP • Uns
Page 13 and 14: Challenge: Same Meaning, Many Varia
Page 15 and 16: Unsupervised Semantic Parsing • U
Page 17 and 18: Unsupervised Semantic Parsing • U
Page 19 and 20: Unsupervised Semantic Parsing • U
Page 21 and 22: Unsupervised Semantic Parsing • E
Page 23: Learning: Hill-Climb Likelihood Ini
Page 27 and 28: Experiments • Evaluate on an end
Page 29 and 30: Induced Ontology (Partial)
Page 31: Conclusion • OntoUSP: Unsupervise