natural-language-processing-3

Entity Extraction using GAN

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

A Unified MRC Framework for Named Entity Recognition

Fei Wu, Yuxian Meng, Jiwei Li, Qinghong Han, Xiaoya Li, Jingrong Feng•Thu Oct 24 2019

This paper proposes to formulate the task of NER as a machine reading comprehension (MRC) task, and naturally tackles the entity overlapping issue in nested NER: the extraction of two overlapping entities with different categories requires answering two independent questions.

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Revisiting Semi-Supervised Learning with Graph Embeddings

R. Salakhutdinov, Zhilin Yang, William W. Cohen•Mon Mar 28 2016

On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, the proposed semi-supervised learning framework shows improved performance over many of the existing models.

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Automatic Labeling for Entity Extraction in Cyber Security

R. A. Bridges, Corinne L. Jones, Michael D. Iannacone, J. Goodall•Wed Aug 21 2013

A very precise method to automatically label text from several data sources by leveraging related, domainspecific, structured data and provide public access to a corpus annotated with cyber-security entities is developed.

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

A. Jorge, Conceição Rocha, R. A. Sinoara, Paula Brito, Carlos Pimenta, S. O. Rezende•Thu Dec 29 2016

A simple and efficient named entity extraction algorithm that relies on flexible pattern matching, part-of-speech tagging and lexical-based rules for NER, developed to process texts written in Portuguese.

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A Hierarchical Framework for Relation Extraction with Reinforcement Learning

Minlie Huang, Tianyang Zhang, Ryuichi Takanobu, Jiexi Liu•Thu Nov 08 2018

This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation and applies a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types.

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

Qianying Liu, Daojian Zeng, Haoran Zhang•Sat Nov 23 2019

A detailed analysis of the reasons behind the inaccurate entity extraction problem is given, and a simple but extremely effective model structure is proposed to solve this problem, called CopyMTL, to allow the model to predict multi-token entities.

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

Bridget T. McInnes, Andriy Mulyar•Mon Apr 20 2020

Multitask-Clinical BERT is developed, a single deep learning model that simultaneously performs 8 clinical tasks spanning entity extraction, personal health information identification, language entailment, and similarity by sharing representations among tasks, and performs competitively with all state-of-the-art task-specific systems.

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Named Entity Extraction with Finite State Transducers

Diego Alexander Huérfano Villalba, Elizabeth León-Guzmán•Fri Jun 19 2020

A named entity tagging system that requires minimal linguistic knowledge and can be applied to more target languages without substantial changes is described and it requires a lineal time for tagging.

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Joint Extraction of Events and Entities within a Document Context

Tom Michael Mitchell, Bishan Yang•Tue May 31 2016

This paper proposes a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document to enable access to document-level contextual information and facilitate context-aware predictions.

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

Hinrich Schütze, Pankaj Gupta, B. Andrassy•Wed Nov 30 2016

A Table Filling Multi-Task Recurrent Neural Network model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies and shows that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance.

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