Applications 4 - Information Extraction and Knowledge-based QA (11/9/2021)
- What are Knowledge Graphs/Ontologies?
- Relation Extraction from Embeddings
- Learning Embeddings from Relations
- Probing Language Models for Knowledge
- Required Reading: Relation Extraction Jurafsky and Martin Chapter 17.2
- Reference: Relation Extraction Survey (Nickel et al. 2016)
- Reference: WordNet (Miller 1995)
- Reference: Cyc (Lenant 1995)
- Reference: DBPedia (Auer et al. 2007)
- Reference: YAGO (Suchanek et al. 2007)
- Reference: Babelnet (Navigli and Ponzetto 2010)
- Reference: Freebase (Bollacker et al. 2008)
- Reference: Wikidata (Vrandečić and Krötzsch 2014)
- Reference: Relation Extraction by Translating Embeddings (Bordes et al. 2013)
- Reference: Relation Extraction with Neural Tensor Networks (Socher et al. 2013)
- Reference: Relation Extraction by Translating on Hyperplanes (Wang et al. 2014)
- Reference: Relation Extraction by Representing Entities and Relations (Lin et al. 2015)
- Reference: Relation Extraction w/ Decomposed Matrices (Xie et al. 2017)
- Reference: Distant Supervision for Relation Extraction (Mintz et al. 2009)
- Reference: Relation Classification w/ Recursive NNs (Socher et al. 2012)
- Reference: Relation Classification w/ CNNs (Zeng et al. 2014)
- Reference: Open IE from the Web (Banko et al. 2007)
- Reference: ReVerb Open IE (Fader et al. 2011)
- Reference: Supervised Open IE (Stanovsky et al. 2018)
- Reference: Universal Schema (Riedel et al. 2013)
- Reference: Joint Entity and Relation Embedding (Toutanova et al. 2015)
- Reference: Distant Supervision for Neural Models (Luo et al. 2017)
- Reference: Relation Extraction w/ Tensor Decomposition (Sutskever et al. 2009)
- Reference: Relation Extraction via. KG Paths (Lao and Cohen 2010)
- Reference: Relation Extraction by Traversing Knowledge Graphs (Guu et al. 2015)
- Reference: Relation Extraction via Differentiable Logic Rules (Yang et al. 2017)
- Reference: Improving Embeddings w/ Semantic Knowledge (Yu et al. 2014)
- Reference: Improving Embeddings w/ Semantic Knowledge (Yu et al. 2014)
- Reference: Retrofitting Word Vectors to Semantic Lexicons (Faruqui et al. 2015)
- Reference: Multi-sense Embedding with Semantic Lexicons (Jauhar et al. 2015)
- Reference: Antonymy and Synonym Constraints for Word Embedding (Mrksic et al. 2016)
- Reference: Language Models as Knowledge Bases? (Petroni et al. 2019)
- Reference: How Can We Know What Language Models Know? (Jiang et al. 2019)
- Reference: AUTOPROMPT: Eliciting Knowledge from Language Models with Automatically Generated Prompts (Shin et al. 2020)
- Reference: GPT Understands, Too (Liu et al. 2021)
- Reference: How Much Knowledge Can You Pack Into the Parameters of a Language Model? (Roberts et al. 2020)
- Reference: X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models (Jiang et al. 2020)
- Reference: REALM: Retrieval-Augmented Language Model Pre-Training (Guu et al. 2020)
- Reference: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al. 2020)
Slides: Knowledge Graph Slides