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Neural Cross-Lingual Entity Linking

AAAI 2018 · Vision-language and multimodal › CLIP and zero-shot · approach: Language models for zero-shot reasoning · 67 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add context LSTMs to the base similarity features (ablation)Neural cross-lingual entity linker · CoNLL 2003 test-bIn-KB accuracy: 92.1 -> 93.0Helped
add LDC to the context-LSTM model (ablation)Neural cross-lingual entity linker · CoNLL 2003 test-bIn-KB accuracy: 93.0 -> 93.4Helped
add MPCM to the context-LSTM and LDC model (ablation)Neural cross-lingual entity linker · CoNLL 2003 test-bIn-KB accuracy: 93.4 -> 94.0Helped
add context LSTMs to the base similarity features (ablation)Neural cross-lingual entity linker · TAC 2010In-KB accuracy: 85.0 -> 86.3Helped
add LDC to the context-LSTM model (ablation)Neural cross-lingual entity linker · TAC 2010In-KB accuracy: 86.3 -> 86.9Helped
add MPCM to the context-LSTM and LDC model (ablation)Neural cross-lingual entity linker · TAC 2010In-KB accuracy: 86.9 -> 87.4Helped
use Multi-CCA instead of LS embeddings (ablation)Neural cross-lingual entity linker · TAC 2015 SpanishLinking accuracy: 81.9 -> 82.3Helped
use Multi-CCA instead of CCA embeddings (ablation)Neural cross-lingual entity linker · TAC 2015 SpanishLinking accuracy: 81.8 -> 82.3Helped
use Multi-CCA instead of LS embeddings (ablation)Neural cross-lingual entity linker · TAC 2015 ChineseLinking accuracy: 84.1 -> 84.4Helped
use Multi-CCA instead of CCA embeddings (ablation)Neural cross-lingual entity linker · TAC 2015 ChineseLinking accuracy: 84.3 -> 84.4Helped

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