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20082022
most citedEnd-to-end neural relation extraction using deep biaffine attention

66 citations · 107 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CL20224 cited

Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation

Thinh Hung Truong, Yulia Otmakhova, Timothy Baldwin +3

Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to…

cs.CL2021

Impact of detecting clinical trial elements in exploration of COVID-19 literature

Simon Šuster, Karin Verspoor, Timothy Baldwin +4

The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature. Although semi-structured information resulting from conc…

cs.CL20193 cited

SemEval-2017 Task 3: Community Question Answering

Preslav Nakov, Doris Hoogeveen, Lluís Màrquez +4

We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Simil…

cs.CL2019

Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings

Zenan Zhai, Dat Quoc Nguyen, Saber A. Akhondi +5

Chemical patents are an important resource for chemical information. However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in pa…

cs.CL201866 cited

End-to-end neural relation extraction using deep biaffine attention

Dat Quoc Nguyen, Karin Verspoor

We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features. The key contribution of our model is to exte…

cs.CL2018

Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition

Zenan Zhai, Dat Quoc Nguyen, Karin Verspoor

We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks. Empirical…