Fine grained Entity Typing using Graph Convolutional Networks

In this presentation, only GCN basics are explained and mention encoder is not.

Transcript

1. 1/13 1 Fine-grained Entity Typing using GCN without predefined typing structure 2019-05-23 @izuna385
2. 2/13 Paper(Baseline) 2 Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (Xiong et al, NAACL ’19) Task : Entity typing(multi-label prediction) RQ : Does Typing-Correlation encoding via GCN contributes Typing prediction? Approach : Add graph propagation layer to prediction module.
3. 3/13 Task and Dataset (Choi et al, ACL ’18) 3 • train/dev/test = 2000 * 3 •
4. 4/13 Motivation for using GCN • Recent model for Entity typing incorporates pre-defined typing structure. • But there are lots of typing which are unseen in KB. • Without pre-defined structure, still label-correlation should be considered. (Shikhar et al, 2018) inconsistent
5. 5/13 GCN(simplest) W0: learnable parameter
6. 6/13 GCN with self loop W0: learnable parameter
7. 7/13 GCN(summarized) 0 https://www.experoinc.com/post/node-classification-by-graph- convolutional-network Adjacent/co-occurrence matrix has structure information. Propagation rule is learned during training.
8. 8/13 Incorporate structure-bias into prediction layer Linear transformation layer :encoded mention mention encoding (In this slide, this module isn’t explained.)
9. 9/13 Incorporate structure-bias into prediction layer Linear transformation layer(learned) :encoded mention
10. 10/13 Incorporate structure-bias into prediction layer Linear transformation layer(learned) :encoded mention • Linear-transformation layer(prediction layer) can be seen as typing embedding matrix. • But, how to incorporate label-correlation?
11. 11/13 Incorporate structure-bias into prediction layer Linear transformation layer :encoded mention Typing co- occurrence information (fixed) Typing matrix Propagation rule :learned :type-type correlation incorporated type-emb matrix
12. 12/13 Evaluation and re-implementation result model P(test) R(test) F(test) Choi et al.(2018) 47.1 24.2 32.0 LabelGCN(paper) 50.3 29.2 36.9 LabelGCN(re-imp) 49.2 28.4 36.0 Hyperparameters are the same as original ones. Epochs are not written, so I stopped at 100 iter. No P-R curve (prob-threshold) tuning.(Threshold=0.5, same as Choi et al.)
13. 13/13 Future work • Ablation study • Apply this model to another dataset/domain (OntoNotes, etc)