Jiaming Xu's repositories

encoder_decoder

Four styles of encoder decoder model by Python, Theano, Keras and Seq2Seq

STC2

Demo code for the paper STC2 which released three short text datasets for clustering and classification

StackOverflow

One short text dataset for classification and clustering extracted from StackOverflow

ASAM

This is the code&dataset for our paper [Modeling Attention and Memory for Auditory Selection in a Cocktail Party Environment. AAAI 2018]

HMN4QA

This is the code&dataset for the paper [Hierarchical Memory Networks for Answer Selection on Unknown Words. COLING 2016]

2016NLPCC_Stance_Detection

This is our solution for NLPCC2016 shared task: Detecting Stance in Chinese Weibo (Task A)

2015KDDCup_MOOC

This is our code for KDDCup2015 on Predicting dropouts in MOOC - , and this code can easily get AUC 0.8589, and the finnal best score is 0.9091.

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2015NDBCCUP_Sougou

This is our code for NDBCCUP2015 on knowledge Extraction from the web - Semantic Relevancy Calculation of Short Texts, and we got Third-Class Award (4/92)

Mem2Seq

Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems

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short-text-hashing-HMTT

This is the code for the paper [Short Text Hashing Improved by Integrating Multi-Granularity Topics and Tags. CICLing 2015]

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AREL

Implementation of the ACL paper "No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling"

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2015JIST_Challenge

This is our solution for JIST2015 Challenge on entity type prediction over linked data. Our solution got Second Place Award (1000RMB) with 0.98435 F1 score.

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chineseocr

yolo3+ocr

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HanLP

中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

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keras-face

face detection, verification and recognition using Keras

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knowledge-driven-dialogue

baseline system of knowledge driven dialogue competition

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py-webrtcvad

Python interface to the WebRTC Voice Activity Detector

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python-vad

py-webrtcvad wrapper for trimming speech clips

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pytorch-seq2seq

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. [IN PROGRESS]

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