sidney_NLP's repositories
acl2019_nested_ner
Source code for paper Neural Architectures for Nested NER through Linearization
BERT-related-papers
BERT-related papers
BERTem
ACL2019论文实现《Matching the Blanks: Distributional Similarity for Relation Learning》
biobert
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
converse_reading_cmr
code, data, and model for ACL-19 paper "Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading"
DAML
Source code for the ACL 2019 paper entitled "Domain Adaptive Dialog Generation via Meta Learning" by Kun Qian and Zhou Yu
DCA
This repository contains code used in the EMNLP 2019 paper "Learning Dynamic Context Augmentation for Global Entity Linking" by Xiyuan Yang et al.
deep_exhaustive_model
An Implementation of Deep Exhaustive Model for Nested NER
dialog-intent-induction
Code and data for paper "Dialog Intent Induction with Deep Multi-View Clustering", Hugh Perkins and Yi Yang, 2019, to appear in EMNLP 2019
FASPell
2019-SOTA简繁中文拼写检查工具：FASPell Chinese Spell Checker (Chinese Spell Check / 中文拼写检错 / 中文拼写纠错 / 中文拼写检查)
FNP
Implementation of the Functional Neural Process models
hanziconv
Hanzi Converter for Traditional and Simplified Chinese
KagNet
Knowledge-Aware Graph Networks for Commonsense Reasoning (EMNLP 19)
LexiconNER
Lexicon-based Named Entity Recognition
lic2019-dureader2.0-rank2
2019语言与智能技术竞赛- Dureader 2.0 机器阅读理解竞赛 Rank2
meta-transfer-learning
TensorFlow and PyTorch implementations of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
ObjectDetectionImbalance
Lists the papers related to imbalance problems in object detection
OpenKP
Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.
PAML
Personalizing Dialogue Agents via Meta-Learning
pytorch-maml
An Implementation of Model-Agnostic Meta-Learning in PyTorch with Torchmeta
pytorch-meta
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
pytorch-struct
A library of vectorized implementations of core structured prediction algorithms.
relation-extraction
Relation Extraction with Semeval 2010 data, i2b2 2010 VA challenge classification data and DDI extraction data.
torchlars
A LARS implementation in PyTorch
Tree-Transformer
Implementation of the paper Tree Transformer