Dependency Parsing On Penn Treebank

LAS

POS

UAS

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

جدول المقارنة

اسم النموذج	LAS	POS	UAS
globally-normalized-transition-based-neural	92.79	97.44	94.61
efficient-second-order-treecrf-for-neural	94.49	-	96.14
deep-biaffine-attention-for-neural-dependency	95.75	-	97.29
second-order-neural-dependency-parsing-with	95.34	-	96.91
generalizing-natural-language-analysis-1	94.70	-	96.44
distilling-an-ensemble-of-greedy-dependency	92.06	97.44	94.26
deep-biaffine-attention-for-neural-dependency	94.22	-	95.87
graph-based-dependency-parsing-with-graph	94.31	97.3	95.97
rethinking-self-attention-an-interpretable	96.26	97.3	97.42
an-improved-neural-network-model-for-joint	93.87	97.97	95.51
structured-training-for-neural-network	92.06	97.3	94.01
recursive-non-autoregressive-graph-to-graph	95.01	-	96.66
head-driven-phrase-structure-grammar-parsing	95.72	97.3	97.20
semi-supervised-sequence-modeling-with-cross	95.02	-	96.61
enhancing-structure-aware-encoder-with	95.92	-	97.30
left-to-right-dependency-parsing-with-pointer	94.43	-	96.04
simple-and-accurate-dependency-parsing-using	91.9	97.44	93.99
training-with-exploration-improves-a-greedy	91.42	97.3	93.56
stack-pointer-networks-for-dependency-parsing	94.19	97.3	95.87
simple-and-accurate-dependency-parsing-using	91.0	97.3	93.1
automated-concatenation-of-embeddings-for-1	95.8	-	97.2
النموذج 22	93.2	-	95.42