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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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Marrying up Regular Expressions with Neural Networks:A Case Study for Spoken Language Understanding
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Population Genetic Diversity and Phylogenetic Characteristics for High-Altitude Adaptive Kham Tibetan Revealed by DNATyperTM 19 Amplification System
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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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Learning with noise:enhance distantly supervised relation extraction with dynamic transition matrix
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A convolution BiLSTM neural network model for Chinese event extraction
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Abstract:
Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling.
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URL: https://eprints.lancs.ac.uk/id/eprint/83783/1/160.pdf https://eprints.lancs.ac.uk/id/eprint/83783/ https://doi.org/10.1007/978-3-319-50496-4_23
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