A very simple framework for state-of-the-art Natural Language Processing (NLP)
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Updated
Aug 7, 2024 - Python
A very simple framework for state-of-the-art Natural Language Processing (NLP)
基于Pytorch和torchtext的知识图谱深度学习框架。
A neural network architecture for NLP tasks, using cython for fast performance. Currently, it can perform POS tagging, SRL and dependency parsing.
Deep Semantic Role Labeling with Self-Attention
Astock
*SEM 2018: Learning Distributed Event Representations with a Multi-Task Approach
Collection of papers on Emotion Cause Analysis
BERT-based nominal Semantic Role Labeling (SRL), both using the Nombank dataset and the Ontonotes dataset.
VerbNet semantic parser and related utilities
Reimplementation of a BERT based model (Shi et al, 2019), currently the state-of-the-art for English SRL. This model implements also predicate disambiguation.
Code for the ACL2021 paper: Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling
[COLING'22] Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".
The Mingled Structured Predictor
Scripts for preprocessing the CoNLL-2005 SRL dataset.
BERT models for semantic relation classification and semantic role labeling
This project is about Template Extraction from a document using NLP Techniques
Encoder-Decoder model for Semantic Role Labeling
An implementation of the paper A Unified Architecture for Semantic Role Labeling and Relation Classification
Semantic Role Labeling Interface and Automatic Annotation Algorithms
SRL deep learning model is based on DB-LSTM which is described in this paper : [End-to-end learning of semantic role labeling using recurrent neural networks](http://www.aclweb.org/anthology/P15-1109)
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