Semantic Information Extraction: Overview and Basic Techniques

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̵ ̵ ̵ Compare DS <strong>and</strong> PB with Set Expansion • Set Expansion: Problem statement Given a list of seed terms in a semantic class • Q = {s 1 , s 2 , …, s k } (e.g. Q={Lent, Epiphany}) To find other members of the class R(t,s i ): the rank of term t • E.g., {Advent, Easter, Christmas…} among the neighbors of s i • Set Expansion with a similarity graph G Select the terms most similar to the seeds
Compare <strong>and</strong> Combine PB & DS (cont.) • Corpus: ClueWeb (500 million English pages) • Five term categories: proper nouns, common nouns, verbs, adjectives, adverbs • Key observations: PB performs better for proper nouns; DS has better performance for other term categories
Page 1 and 2: Semantic Information Extraction: Ov
Page 3 and 4: ̵ ̵ ̵ ̵ ̵ Information Extracti
Page 5 and 6: ̵ ̵ Semantic Information Extracti
Page 7 and 8: IE Task: Named Entity Extraction
Page 9 and 10: IE Task: Relation & Event Extractio
Page 11 and 12: ̵ ̵ ̵ ̵ Semantic Class Mining
Page 13 and 14: ̵ PB Implementation • RASC minin
Page 15 and 16: ̵ ̵ ̵ ̵ ̵ ̵ ̵ ̵ Distributio
Page 17: DS Implementation DS approaches imp
Page 21 and 22: Samples (Query: Apple) PB results D
Page 23 and 24: Related Papers • Harris, 1985 (in
Page 25 and 26: Semantic Hierarchy Construction
Page 27 and 28: Subtask: TermLabel • Approach: Pa
Page 29 and 30: ̵ ̵ Subtask: TermLabel (cont.)
Page 31 and 32: Related Papers • Hearst, COLING
Page 33 and 34: Semantic Attributes Attr2 Attr3 Att
Page 35 and 36: ̵ ̵ ̵ ̵ ̵ ̵ Attribute Name Ex
Page 37 and 38: Table Mining References • G. Lima
Page 39 and 40: ̵ ̵ General Relations • Relatio
Page 41 and 42: ̵ ̵ ̵ ̵ Supervised Learning •
Page 43 and 44: ̵ ̵ ̵ ̵ Kernel based Methods
Page 45 and 46: ̵ ̵ Tree Kernel for RDC • Convo
Page 47 and 48: ̵ ̵ Semi-Supervised Learning •
Page 49 and 50: Weakly Supervision • Handful of s
Page 51 and 52: Unsupervised Learning • Automatic
Page 53 and 54: References for General Relation Min
Page 55 and 56: Demo • “Find needles in a hayst

Semantic Information Extraction: Overview and Basic Techniques

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