Estimating probability distribution is one of the core issues in the NLP
field. However, in both deep learning (DL) and pre-DL eras, unlike the vast
applications of linear-chain CRF in sequence labeling tasks, very few works
have applied tree-structure CRF to constituency parsing, mainly due to the
complexity and inefficiency of the inside-outside algorithm. This work presents
a fast and accurate neural CRF constituency parser. The key idea is to batchify
the inside algorithm for loss computation by direct large tensor operations on
GPU, and meanwhile avoid the outside algorithm for gradient computation via
efficient back-propagation. We also propose a simple two-stage
bracketing-then-labeling parsing approach to improve efficiency further. To
improve the parsing performance, inspired by recent progress in dependency
parsing, we introduce a new scoring architecture based on boundary
representation and biaffine attention, and a beneficial dropout strategy.
Experiments on PTB, CTB5.1, and CTB7 show that our two-stage CRF parser
achieves new state-of-the-art performance on both settings of w/o and w/ BERT,
and can parse over 1,000 sentences per second. We release our code at
this https URL

本研究旨在提出一种快速准确的神经 CRF 成分句法分析器，采用内部算法批处理、边界表示和双仿射关注等手段，提高了句法分析的效率和性能，并在三个数据集上达到了最新的最高性能。

快速准确的神经 CRF 短语结构句法分析

Fast and Accurate Neural CRF Constituency Parsing

We propose a neural network model for joint extraction of named entities and
relations between them, without any hand-crafted features. The key contribution
of our model is to extend a BiLSTM-CRF-based entity recognition model with a
deep biaffine attention layer to model second-order interactions between latent
features for relation classification, specifically attending to the role of an
entity in a directional relationship. On the benchmark "relation and entity
recognition" dataset CoNLL04, experimental results show that our model
outperforms previous models, producing new state-of-the-art performances.

本文提出一个神经网络模型，用于联合抽取命名实体和它们之间的关系，无需手动设计特征。该模型采用深度双仿射关注层扩展了 BiLSTM-CRF 实体识别模型，以建模关系分类中潜在特征的二阶交互。在 benchmark 数据集 CoNLL04 上进行实验，结果显示该模型的表现优于之前的模型，创造了新的最好表现。