natural-language-processing-11
Relation Extraction
3260 papers • 126 benchmarks • 313 datasets
Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization. Source: Deep Residual Learning for Weakly-Supervised Relation Extraction
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DocRED
DocRED
TACRED
TACRED
SemEval-2010 Task-8
SemEval-2010 Task-8
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5 papers 119,635
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Sungdong Kim, Jinhyuk Lee, WonJin Yoon, Donghyeon Kim, Sunkyu Kim, Chan Ho So, Jaewoo Kang•Thu Jan 24 2019
This article introduces BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora that largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre- trained on biomedical Corpora.
