TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
An implementation of learning with entity trigger as explanations for the Named Entity Recognition (NER) task (ACL 2020).
Lean-Life: A Label-Efficient Annotation Framework Towards Learning from Explanation
A web based framework for obtaining human explanation annotations (ACL 2020, System Demo).
NExT: Learning from Explanations with Neural Module Execution Tree
An implementation of the proposed Neural Module Execution Tree frameworks for learning from explanations (ICLR 2020).
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
An implementation of the proposed neural rule grounding framework (WWW 2020).
HiExpl: Hierarchical explanations of neural sequence model predictions
Pytorch implementation of "Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models".
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
An implementation of the proposed KagNet model for commonsense reasoning (EMNLP-IJCNLP 2019).
CPL: Collaborative Policy Learning for Open Knowledge Graph Reasoning
TensorFlow implementation of EMNLP 2019 paper Collaborative Policy Learning for Open Knowledge Graph Reasoning. Moving to PyTorch.
Recurrent Event Network: Global Structure Inference over Temporal Knowledge Graph
Pytorch implementation for Recurrent Event Network: Global Structure Inference over Temporal Knowledge Graph (ICLR-RLGM 2019), which is an autoregressive model to infer graph structures at unobserved times on temporal knowledge graphs (extrapolation problem).
AlpacaTag: Active Learning-based Tagging Framework.
AlpacaTag is an open-source web-based data annotation framework for sequence tagging tasks, such as named-entity recognition (NER).
DS-RelationExtraction: Distantly-supervised Relation Extraction with Knowledge Bases
This repository puts together recent models and data sets for sentence-level relation extraction using knowledge bases (i.e., distant supervision). In particular, it contains the source code for WWW'17 paper CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases. Task: Given a text corpus with entity mentions detected and heuristically labeled using distant supervision, the task aims to identify relation types/labels between a pair of entity mentions based on the sentence context where they co-occur.