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Use of supervised machine learning to detect abuse of COVID-19 related domain names()
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In: Comput Electr Eng (2022)
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Modularized Interaction Network for Named Entity Recognition ...
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Measurement of the top quark mass with lepton+jets final states using $\mathrm {p}$ $\mathrm {p}$ collisions at $\sqrt{s}=13\,\text {TeV} $
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In: http://infoscience.epfl.ch/record/275278 (2020)
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Genetic diversity and phylogenetic structure of four Tibeto‐Burman‐speaking populations in Tibetan‐Yi corridor revealed by insertion/deletion polymorphisms
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Genetic diversity and phylogenetic analysis of Chinese Han and Li ethnic populations from Hainan Island by 30 autosomal insertion/deletion polymorphisms ...
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Genetic diversity and phylogenetic analysis of Chinese Han and Li ethnic populations from Hainan Island by 30 autosomal insertion/deletion polymorphisms ...
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Assessment of the quality and content of clinical practice guidelines for post-stroke rehabilitation of aphasia
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Genetic structure and forensic characteristics of Tibeto-Burman-speaking Ü-Tsang and Kham Tibetan Highlanders revealed by 27 Y-chromosomal STRs
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Measurement of prompt and nonprompt charmonium suppression in $\text {PbPb}$ collisions at 5.02 $\,\text {Te}\text {V}$
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In: Eur.Phys.J.C ; https://hal.archives-ouvertes.fr/hal-01833739 ; Eur.Phys.J.C, 2018, 78 (6), pp.509. ⟨10.1140/epjc/s10052-018-5950-6⟩ (2018)
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| 10 |
Marrying up Regular Expressions with Neural Networks:A Case Study for Spoken Language Understanding
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| 11 |
Population Genetic Diversity and Phylogenetic Characteristics for High-Altitude Adaptive Kham Tibetan Revealed by DNATyperTM 19 Amplification System
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| 12 |
Measurement of prompt and nonprompt charmonium suppression in $\text {PbPb}$ collisions at 5.02 $\,\text {Te}\text {V}$
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| 13 |
Learning with noise:enhance distantly supervised relation extraction with dynamic transition matrix
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Abstract:
Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transition matrix can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise. We thoroughly evaluate our approach under a wide range of extraction scenarios. Experimental results show that our approach consistently improves the extraction results and outperforms the state-of-the-art in various evaluation scenarios.
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URL: https://eprints.lancs.ac.uk/id/eprint/85778/
https://doi.org/10.18653/v1/P17-1040
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A convolution BiLSTM neural network model for Chinese event extraction
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