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Nora Tang's Projects

2xinter icon 2xinter

This project includes the code implemented with PyTorch and the paper 'Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection' published at LREC-COLING 2024.

aaci icon aaci

This project includes the code implemented with PyTorch and the paper 'Improving Long-tail Relation Extraction via Adaptive Adjustment and Causal Inference' published in the Neurocomputing 2023 journal.

bayesian-word-vectors icon bayesian-word-vectors

An implementation of the algorithm described in Barkan's 2016 paper "Bayesian Neural Word Embedding"

nlp-journey icon nlp-journey

NLP 相关的一些文档、论文及代码, 包括主题模型(Topic Model)、词向量(Word Embedding)、命名实体识别(Named Entity Recognition)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算、机器翻译(Machine Translation)等,涉及到各种与nlp相关的算法,基于keras和tensorflow。

nlp_ability icon nlp_ability

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识,包括面试题,各种基础知识,工程能力等等,提升核心竞争力

nlpwork icon nlpwork

这个工程主要实现自然语言处理的多个基本任务,不通过掉包的方式。(包括分词、词性标注、停用词过滤、命名实体识别、句法分析、情感分析等)

prml icon prml

PRML algorithms implemented in Python

readme icon readme

README文件语法解读,即Github Flavored Markdown语法介绍

sentimentanalysis-book-lstm icon sentimentanalysis-book-lstm

这里是利用python3.6搭建tensorflow1.8框架编程实现的一层、两层以及双向LSTM模型,且对部分超参数进行灵敏度分析,最终可在tensorbosrd上查看实验结果的工程。

struct-learning-with-flow icon struct-learning-with-flow

PyTorch Implementation of "Unsupervised Learning of Syntactic Structure with Invertible Neural Projections" (EMNLP 2018)

textclassification-meituan-featureextraction-classifier icon textclassification-meituan-featureextraction-classifier

该工程是利用python3.6进行“特征提取+分类器”来实现美团评论的文本二分类问题。在特征提取部分提取了6种特征,分类器选择了python里面的包xgboost和lightGBM分别实现提升树和GBDT(梯度提升决策树)。

weibospider icon weibospider

:zap: A distributed crawler for weibo, building with celery and requests.

word2vec icon word2vec

Automatically exported from code.google.com/p/word2vec

wordembeddings-evaluation-method icon wordembeddings-evaluation-method

该工程是利用python3.6搭建tensorflow1.8框架编程实现词向量的各种评估实验。包括衡量词向量的“语义质量”的实验(词类比实验、词相似度实验、命名实体识别),衡量词向量的“结合情感信息质量”的实验(词语级情感分析、句子级情感分析)

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