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20192025
most citedGPT-NER: Named Entity Recognition via Large Language Models

146 citations · 510 across the 33 of their papers we have counts for

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Showing 2019 · cs.CLShow all

9 papers · 2 filters

cs.CL2019

Dice Loss for Data-imbalanced NLP Tasks

Xiaoya Li, Xiaofei Sun, Yuxian Meng +4

Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and th…

cs.CL2019

Coreference Resolution as Query-based Span Prediction

Wei Wu, Fei Wang, Arianna Yuan +2

In this paper, we present an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in machine reading compr…

cs.CL2019★ 8 cited

Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs

Yuxian Meng, Xiangyuan Ren, Zijun Sun +4

In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than…

cs.CL2019

A Unified MRC Framework for Named Entity Recognition

Xiaoya Li, Jingrong Feng, Yuxian Meng +4

The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately dev…

cs.CL2019

Query-Based Named Entity Recognition

Yuxian Meng, Xiaoya Li, Zijun Sun +1

In this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of ext…

cs.CL2019

DSReg: Using Distant Supervision as a Regularizer

Yuxian Meng, Muyu Li, Xiaoya Li +2

In this paper, we aim at tackling a general issue in NLP tasks where some of the negative examples are highly similar to the positive examples, i.e., hard-negative examples. We pro…