Learning Relatedness between Types with Prototypes for Relation Extraction

1 code implementation • EACL 2021 • Lisheng Fu, Ralph Grishman

We propose to use prototypical examples to represent each relation type and use these examples to augment related types from a different dataset.

Multi-Task Learning Relation +1

A Case Study on Learning a Unified Encoder of Relations

no code implementations • WS 2018 • Lisheng Fu, Bonan Min, Thien Huu Nguyen, Ralph Grishman

Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema.

Knowledge Base Population Relation +1

Distantly Supervised Attribute Detection from Reviews

no code implementations • WS 2018 • Lisheng Fu, Pablo Barrio

This work aims to detect specific attributes of a place (e. g., if it has a romantic atmosphere, or if it offers outdoor seating) from its user reviews via distant supervision: without direct annotation of the review text, we use the crowdsourced attribute labels of the place as labels of the review text.

Attribute Entity Extraction using GAN +1

Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network

no code implementations • IJCNLP 2017 • Lisheng Fu, Thien Huu Nguyen, Bonan Min, Ralph Grishman

Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier.

Relation Relation Extraction +1

A Two-stage Approach for Extending Event Detection to New Types via Neural Networks

no code implementations • WS 2016 • Thien Huu Nguyen, Lisheng Fu, Kyunghyun Cho, Ralph Grishman

Domain Adaptation Event Detection +2

An Efficient Active Learning Framework for New Relation Types

no code implementations • IJCNLP 2013 • Lisheng Fu, Ralph Grishman

Active Learning Question Answering +2