Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Last update: Nov 08, 2022

Overview

crfsrl

Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Zhenghua Li, Guohong Fu, Min Zhang. Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments. 2021. [arxiv]

Setup

The following packages should be installed:

SuPar: == 1.1.3
PyTorch: >= 1.7
Transformers: >= 4.0

Run the following scripts to obtain the training data. Please make sure PTB and OntoNotes are available:

bash scripts/conll05.sh PTB=<path-to-ptb>             SRL=data
bash scripts/conll12.sh ONTONOTES=<path-to-ontonotes> SRL=data

Run

Try the following commands to train first-order CRF and second-order CRF2o models:

# LSTM
# CRF
python -u crf.py   train -b -c configs/conll05.crf.srl.lstm.char-lemma.ini   -d 0 -f char lemma -p exp/conll05.crf.srl.lstm.char-lemma/model
# CRF2o
python -u crf2o.py train -b -c configs/conll05.crf2o.srl.lstm.char-lemma.ini -d 0 -f char lemma -p exp/conll05.crf2o.srl.lstm.char-lemma/model
# BERT finetuning
# CRF
python -u crf.py   train -b -c configs/conll05.crf.srl.bert.ini   -d 0 -p exp/conll05.crf.srl.bert/model   --batch-size=1000 --encoder bert --bert bert-large-cased 
# CRF2o
python -u crf2o.py train -b -c configs/conll05.crf2o.srl.bert.ini -d 0 -p exp/conll05.crf2o.srl.bert/model --batch-size=1000 --encoder bert --bert bert-large-cased

To do evaluation:

# end-to-end
python -u crf.py   evaluate -c configs/conll05.crf.srl.bert.ini  -d 0 -p exp/conll05.crf.srl.bert/model    --data data/conll05/test.conllu
# w/ gold predicates
python -u crf.py   evaluate -c configs/conll05.crf.srl.bert.ini  -d 0 -p exp/conll05.crf.srl.bert/model    --data data/conll05/test.conllu --prd

To make predictions:

python -u crf.py   predict  -c configs/conll05.crf.srl.bert.ini   -d 0 -p exp/conll05.crf.srl.bert/model   --data data/conll05/test.conllu --pred pred.conllu
bash scripts/eval.sh pred=pred.conllu gold=data/conll05/test.conllu

Contact

If you have any questions, feel free to contact me via emails.

Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Related tags

Overview

crfsrl

Setup

Run

Contact

Owner

Yu Zhang

Quantifiers and Negations in RE Documents

Various capabilities for static malware analysis.

NLP-Project - Used an API to scrape 2000 reddit posts, then used NLP analysis and created a classification model to mixed succcess

Script and models for clustering LAION-400m CLIP embeddings.

Code for the ACL 2021 paper "Structural Guidance for Transformer Language Models"

neural network based speaker embedder

Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers"

Spacy-ginza-ner-webapi - Named Entity Recognition API with spaCy and GiNZA

中文空间语义理解评测

code for modular summarization work published in ACL2021 by Krishna et al

I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision

Estimation of the CEFR complexity score of a given word, sentence or text.

Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products

CPC-big and k-means clustering for zero-resource speech processing

Built for cleaning purposes in military institutions

Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

A telegram bot to translate 100+ Languages

BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia.