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Framework Overview
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Classroom Contents
Scientific Text Mining and Knowledge Graphs - Part 2-1
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- 1 Why Phrase Mining?
- 2 Phrase Mining: A Keystone
- 3 Quality Phrase Mining from Massive Domain-Specific Corpora
- 4 Quality Estimation using Expert Labels
- 5 Phrasal Segmentation using Viterbi Algo
- 6 SegPhrase (SIGMOD'15): Quality Estimation Phrasal Segmentation
- 7 SegPhrase (SIGMOD'15): Reliance on Expert-Provided Labels
- 8 AutoPhrase (TKDE'18): Negative Sampling from Noisy Negative Pool
- 9 Phrase Mining: Empirical Evaluation - Precision Recall Curve
- 10 AutoPhrase (TKDE'18): Results of Chinese Phrases from Wiki Articles
- 11 What's Named Entity Recognition?
- 12 Supervised Methods: Training Data
- 13 Supervised Methods: Neural Models
- 14 "Data-Driven" Philosophy
- 15 What's (Neural) Language Model?
- 16 Neural LM: Example Generations
- 17 BERT: Introduce Transformer
- 18 Questions
- 19 Distantly Supervised NER Methods
- 20 SwellShark: Distantly Supervised Typin
- 21 AutoNER: Dual Dictionaries
- 22 AutoNER: Tailored Neural Model
- 23 Comparison - Biomedical Domain
- 24 Summary & Q&A
- 25 Meta-Pattern Mining for Information Extraction
- 26 Our Meta-Pattern Methodology
- 27 Grouping Synonymous Patterns
- 28 Adjusting Types in Meta Patterns for Appropriate Granularity
- 29 PENNER: Pattern-Enhanced Nested Name Entity Recognition in Biomedical Literature
- 30 Framework Overview
- 31 Weakly-supervised Pattern Expansion
- 32 Comparison with Pub Tator
