sudarshan77's Stars
ajesujoba/UNIQORN
quaizarv/QA-Notes
Notes on building a Question Answering System
to314as/Question-answering-with-Wikipedia---NLP-project-2019
We propose and explore a QA system based on data form Wikipedia and the Stanford Question Answering Dataset. We show different approaches to document retrival as well as reading. Including tf-idf and term frequency for retrival. Language models such as word2vec/glove, infersent(sentence2vec by fb research),... Furthermore to we apply different supervised as well as unsupervised techniques for the question answering aspect.
RichardHGL/WSDM2021_NSM
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals. WSDM 2021.
salesforce/rng-kbqa
jishnujayakumar/MLRC2020-EmbedKGQA
This is the code for the MLRC2020 challenge w.r.t. the ACL 2020 paper Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
xwhan/Knowledge-Aware-Reader
PyTorch implementation of the ACL 2019 paper "Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader"
lukasgarbas/nlp-text-emotion
Multi-class sentiment analysis lstm, finetuned bert
zhijing-jin/nlp-phd-global-equality
A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP
nipunam/FactChecker
Answers your questions
zcgzcgzcg1/MRC_book
《机器阅读理解:算法与实践》代码
nlpdata/external
Improving Question Answering with External Knowledge
libertatis/mrc-cbt
ASReader - machine reading comprehension on cbt datasets
rkadlec/asreader
This is an implementation of the Attention Sum Reader model as presented in "Text Comprehension with the Attention Sum Reader Network" available at http://arxiv.org/abs/1603.01547.
msdejong/ASReader
xxnahhhh/ASReader
ASReader with tensorflow
cairoHy/attention-sum-reader
Implementation of the attention-sum reader using tensorflow and keras.
cooelf/AwesomeMRC
IJCAI 2021 Tutorial & code for Retrospective Reader for Machine Reading Comprehension (AAAI 2021)
bishanyang/kblstm
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading