原始的BiaffineParser使用词性Embedding作为模型编码器的辅助输入,这里SuPar还提供了利用CharLSTM或者BERT产生的字表示或者子词表示,来代替词性.其中CharLSTM是默认的选项,避免了额外的词性标注器来生成词性的麻烦,此外相比BERT更加的高效.SuPar中的BERT模块是基于transformers中的预训练模型来产生特征.由于其中的预训练模型互相...
$ git clone https://github.com/chantera/biaffineparser $ cd biaffineparser $ pip install -r requirements.txtUsageTrainingusage: main.py train [-h] --train_file FILE [--eval_file FILE] [--embed_file FILE] [--max_steps NUM] [--eval_interval NUM] [--batch_size NUM] [--learning_...
Biaffine dependency parser. classhanlp.components.parsers.biaffine.biaffine_sdp.BiaffineSemanticDependencyParser[source]¶ Implementation of “Stanford’s graph-based neural dependency parser at the conll 2017 shared task” (Dozat et al. 2017) and “Establishing Strong Baselines for the New Decade” ...
BERT+Transformer+Biaffine dependency parser Update [2020-04-23] 修复数据加载中一个bug,在use_cache=True时可大幅度缩短数据加载准备耗时 以上结果均为在Semeval-2016-Test集上测试得到的LAS 详细结果见:metrics记录 Semeval-2016 Task9数据集 原始评测数据集:HIT-SCIR/SemEval-2016: SemEval-2016 Task 9: Ch...
代码开源在:https://github.com/tdozat/Parser-v1 需要TensorFlow-0.10才能运行,比较古老: # Linux # export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl # Mac exportTF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu...
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser...
Our biaffine AM dependency parser significantly outperforms the state-of-the-art, performing at F1 = 73.5% for component identification and F1 = 46.4% for relation identification. One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results. ...
biaffine_dep¶ Biaffine dependency parser. classhanlp.components.parsers.biaffine.biaffine_dep.BiaffineDependencyParser[source]¶ Biaffine dependency parsing (Dozat & Manning 2017). build_criterion(**kwargs)[source]¶ Implement this method to build criterion (loss function). ...
项目地址为https://github.com/yzhangcs/parser 上述解析器对应的预训练模型,可以直接加载,用来方便地解析依存句法树或者成分句法树. 此外,这个包中还包含了一系列流行的算法的实现,比如MST (ChuLiu/Edmonds),Eisner,CKY,MatrixTree以及TreeCRF等等. 原始的Biaffine Parser使用词性Embedding作为模型编码器的辅助输入,这...