This course focuses on scientific text mining and knowledge graphs, covering topics such as phrase mining, quality estimation, phrasal segmentation, named entity recognition, neural language models, and meta-pattern mining for information extraction. The course aims to teach students the skills needed to extract valuable insights from massive domain-specific corpora using various techniques and tools. The teaching method includes lectures, examples, and empirical evaluations. This course is intended for individuals interested in text mining, natural language processing, and knowledge graph construction.
Scientific Text Mining and Knowledge Graphs - Part 2-1
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Why Phrase Mining?
Phrase Mining: A Keystone
Quality Phrase Mining from Massive Domain-Specific Corpora
Quality Estimation using Expert Labels
Phrasal Segmentation using Viterbi Algo
SegPhrase (SIGMOD'15): Quality Estimation Phrasal Segmentation
SegPhrase (SIGMOD'15): Reliance on Expert-Provided Labels
AutoPhrase (TKDE'18): Negative Sampling from Noisy Negative Pool
Phrase Mining: Empirical Evaluation - Precision Recall Curve
AutoPhrase (TKDE'18): Results of Chinese Phrases from Wiki Articles
What's Named Entity Recognition?
Supervised Methods: Training Data
Supervised Methods: Neural Models
"Data-Driven" Philosophy
What's (Neural) Language Model?
Neural LM: Example Generations
BERT: Introduce Transformer
Questions
Distantly Supervised NER Methods
SwellShark: Distantly Supervised Typin
AutoNER: Dual Dictionaries
AutoNER: Tailored Neural Model
Comparison - Biomedical Domain
Summary & Q&A
Meta-Pattern Mining for Information Extraction
Our Meta-Pattern Methodology
Grouping Synonymous Patterns
Adjusting Types in Meta Patterns for Appropriate Granularity
PENNER: Pattern-Enhanced Nested Name Entity Recognition in Biomedical Literature
Framework Overview
Weakly-supervised Pattern Expansion
Comparison with Pub Tator
Taught by
Association for Computing Machinery (ACM)
