zoeNantes
a postdoc
National Institute of Advanced Industrial Science and Technology, JapanChina; France; Japan
zoeNantes's Stars
Ananyaiitbhilai/Text2Triple-LLM-Agent
[ESWC '24] This repo is official implementation for the paper "Towards Harnessing Large Language Models as Autonomous Agents for Semantic Triple Extraction from Unstructured Text"
amazon-science/page-link-path-based-gnn-explanation
AndMastro/protein-ligand-GNN
This repository contains the code for the work on protein-ligand interaction with GNNs and XAI
RManLuo/GSNOP
Official code implementation for WSDM 23 paper Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs.
susheels/tgrank
CRIPAC-DIG/HGLS
[WWW 2023] The source code of "Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning"
KuroginQin/OpenTLP
[ACM Computing Surveys'23] Implementations or refactor of some temporal link prediction/dynamic link prediction methods and summary of related open resources for survey paper "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review" which has been accepted by ACM Computing Surveys.
salesforce/causalai
Salesforce CausalAI Library: A Fast and Scalable framework for Causal Analysis of Time Series and Tabular Data
bharathgs/Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
ioanabica/HTCE-learners
Code for NeurIPS 2022 paper: "Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation" by I. Bica, M. van der Schaar
alwaysmodest/A-Survey-of-Deep-Causal-Models-and-Their-Industrial-Applications
kim-hyunsu/CEVAE-pyro
CEVAE(Causal Effect Variational AutoEncoder) written with pytorch and pyro.
daac-tools/find-simdoc
Finding all pairs of similar documents time- and memory-efficiently
Waste-Wood/ReCo
[EMNLP 2022 Long Paper] ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
causenet-org/CIKM-20
CauseNet: Towards a Causality Graph Extracted from the Web
DerwenAI/disparity_filter
Implements a disparity filter in Python, based on graphs in NetworkX, to extract the multiscale backbone of a complex weighted network (Serrano, et al., 2009)
rapidfuzz/RapidFuzz
Rapid fuzzy string matching in Python using various string metrics
Text2TCS/Towards-Learning-Terminological-Concept-Systems
A pipeline approach to automatically extract terminological concept systems from text. We use multilingual neural language models to extract terms and their relations on a an intra-sentence level.
semantalytics/awesome-semantic-web
A curated list of various semantic web and linked data resources.
itorr/nbnhhsh
😩「能不能好好说话?」 拼音首字母缩写翻译工具
NExTplusplus/TAT-QA
TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.
czyssrs/FinQA
Data and code for EMNLP 2021 paper "FinQA: A Dataset of Numerical Reasoning over Financial Data"
zhijing-jin/CausalNLP_Papers
A reading list for papers on causality for natural language processing (NLP)
shaialon/elasticsearch-gdelt
Elasticsearch 6.x + Node.js - Visualize Gdelt data with Kibana & Elastic: http://www.gdeltproject.org/
alex9smith/gdelt-doc-api
A Python client for the GDELT 2.0 Doc API
yseop/YseopLab
rohangiriraj/CausalKG
Causality in Knowledge Graphs
GoPeaks-AI/CausTator1.0
CausTator (Causality Annotator) 1.0 is free annotation software for users who have no programming background. The annotated documents can be used directly for most Natural Language Processing (NLP) tasks. CausTator 1.0 works on any IE browser at a local computer environment, which protects privacy of the annotated documents. It automatically detects sentences from textual documents based on a user-defined list of trigger words and stop words, then allows users to tag phrases within these sentences such as causes and outcomes, and finally produces the annotated document for downloads.
songjiang0909/awesome-knowledge-graph-construction
c-box/causalEval
Code for ACL 2022 long paper: Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View