Entity Extraction using GAN

22 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Revisiting Semi-Supervised Learning with Graph Embeddings

tkipf/gcn 29 Mar 2016

We present a semi-supervised learning framework based on graph embeddings.

A Unified MRC Framework for Named Entity Recognition

ShannonAI/mrc-for-flat-nested-ner ACL 2020

Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.

Automatic Labeling for Entity Extraction in Cyber Security

stucco/auto-labeled-corpus 22 Aug 2013

Timely analysis of cyber-security information necessitates automated information extraction from unstructured text.

PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese

LIAAD/py-pampo 30 Dec 2016

This paper deals with the entity extraction task (named entity recognition) of a text mining process that aims at unveiling non-trivial semantic structures, such as relationships and interaction between entities or communities.

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

truthless11/HRL-RE 9 Nov 2018

The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.

CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

WindChimeRan/CopyMTL 24 Nov 2019

The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction.

MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning

AndriyMulyar/multitasking_transformers 21 Apr 2020

Clinical notes contain an abundance of important but not-readily accessible information about patients.

Named Entity Extraction with Finite State Transducers

dahuerfanov/NER-System 20 Jun 2020

We describe a named entity tagging system that requires minimal linguistic knowledge and can be applied to more target languages without substantial changes.

Joint Extraction of Events and Entities within a Document Context

bishanyang/EventEntityExtractor NAACL 2016

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon.

Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

pgcool/TF-MTRNN COLING 2016

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.