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Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowledge, and require considerable human efforts on
feature-engineering. To tackle these problems, in this work, we formulate ACBD
as a sequence labeling problem and propose a variety of Recurrent Neural
Network (RNN) based methods, which do not use domain specific or handcrafted
features beyond the relative position of the sentence in the document. In
particular, we propose a novel joint RNN model that can predict whether
sentences are argumentative or not, and use the predicted results to more
precisely detect the argument component boundaries. We evaluate our techniques
on two corpora from two different genres; results suggest that our joint RNN
model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201
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In the dinuclear salen-type title complex, [CuNa(BF4)(C18H18N2O4)(CH3OH)], the CuII atom is chelated by two O atoms and two N atoms of the deprotonated Schiff base in a square-planar geometry. The Na atom is seven-coordinate as it is linked to four O atoms of the same Schiff base ligand, one O atom of the methanol and two tetraÂfluoridoÂborate F atoms. The remaining two F atoms of the anion are disordered over two sites in a 0.598 (18):0.402 (18) ratio
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