We propose a novel neural network model for joint part-of-speech (POS)
tagging and dependency parsing. Our model extends the well-known BIST
graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating
a BiLSTM-based tagging component to produce automatically predicted POS tags
for the parser. On the benchmark English Penn treebank, our model obtains
strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%
absolute improvements to the BIST graph-based parser, and also obtaining a
state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental
results on parsing 61 "big" Universal Dependencies treebanks from raw texts
show that our model outperforms the baseline UDPipe (Straka and Strakov\'a,
2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS
score. In addition, with our model, we also obtain state-of-the-art downstream
task scores for biomedical event extraction and opinion analysis applications.
Our code is available together with all pre-trained models at:
this https URL

本文提出了一种新的神经网络模型，用于联合部分词性标注和依赖解析，将 BiLSTM 标记组件结合到 BIST 依赖解析器中，可对英语 Penn 语料库进行联合学习，模型在 LUS 和 UAS 评分上分别达到 94.51% 和 92.87%，并在多个评测数据集上都取得了最佳表现。

联合词性标注和依存关系分析的改进神经网络模型

An improved neural network model for joint POS tagging and dependency  parsing

Generative neural models have recently achieved state-of-the-art results for
constituency parsing. However, without a feasible search procedure, their use
has so far been limited to reranking the output of external parsers in which
decoding is more tractable. We describe an alternative to the conventional
action-level beam search used for discriminative neural models that enables us
to decode directly in these generative models. We then show that by improving
our basic candidate selection strategy and using a coarse pruning function, we
can improve accuracy while exploring significantly less of the search space.
Applied to the model of Choe and Charniak (2016), our inference procedure
obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior
state-of-the-art results for single-model systems.

通过改进候选选择和使用粗略的修剪功能，我们描述了一种可行的搜索过程，及其用于 Choe 和 Charniak（2016）模型的推理过程在 Penn Treebank 的 23 节上能够获得 92.56 F1，超过以前的最先进结果。

生成神经语法分析的有效推论

Effective Inference for Generative Neural Parsing

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has
been shown to be very effective for tagging sequential data, e.g. speech
utterances or handwritten documents. While word embedding has been demoed as a
powerful representation for characterizing the statistical properties of
natural language. In this study, we propose to use BLSTM-RNN with word
embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank
WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is
achieved. Without using morphological features, this approach can also achieve
a good performance comparable with the Stanford POS tagger.

使用词嵌入的双向 LSTM 循环神经网络模型（BLSTM-RNN）在词性标注任务中表现出色，可以达到 97.40％的准确率，而且不需要使用形态学特征，同时具备与斯坦福词性标注器相当的性能。

双向长短时记忆循环神经网络进行词性标注

Part-of-Speech Tagging with Bidirectional Long Short-Term Memory  Recurrent Neural Network

We present structured perceptron training for neural network transition-based
dependency parsing. We learn the neural network representation using a gold
corpus augmented by a large number of automatically parsed sentences. Given
this fixed network representation, we learn a final layer using the structured
perceptron with beam-search decoding. On the Penn Treebank, our parser reaches
94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge
is the best accuracy on Stanford Dependencies to date. We also provide in-depth
ablative analysis to determine which aspects of our model provide the largest
gains in accuracy.

通过结构化感知机训练和使用大量自动解析的句子来学习神经网络表示，我们的解析器在 Penn Treebank 数据集上达到了 94.26％的未标记和 92.41％的标记附加精度，是迄今为止在 Stanford Dependencies 上最佳的精度，并提供深入的剖析分析以确定模型的哪些方面提供了最大的准确性增益。