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周奇's Projects

2016_adl icon 2016_adl

Applied Deep Learning @National Taiwan University

agdistis icon agdistis

AGDISTIS - Agnostic Named Entity Disambiguation

agriculture-knowledgegraph-data icon agriculture-knowledgegraph-data

对知识库Wikidata的爬虫以及数据处理脚本 将三元组关系对齐到语料库的脚本 获取知识图谱数据的脚本

albert_zh icon albert_zh

海量中文预训练ALBERT模型, A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS

algorithm_interview_notes-chinese icon algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

algorithms icon algorithms

Bug-tracking for Jeff's algorithms book, notes, etc.

aml icon aml

Notebooks for the Algorthmic Machine Learning class @ Eurecom

analysis-wikipedia-entities icon analysis-wikipedia-entities

Goal: To understand the Wikipedia dataset, especially the entity info boxes. Task: We have taken the Wikipedia dump. Our aim is to extract information about various entity types. The steps for this task are as follows: 1. Given the Wikipedia dump, gather all the pages from Wikipedia with Info boxes on them. 2. Find the set of all possible entity types on Wikipedia 3. Find the set of all possible attributes that can be associated with any entity type on Wikipedia. 4. From a few values of these attributes, infer the data type of these attributes as one of the following: String, set of strings, duration, number, set of durations, date, other. 5. Find various units that can be used to express the value of a numeric attribute. E.g., for “height” attribute of “person” entities, the units could be “cms, inches” 6. For numeric attributes, find typical ranges (using the most popular unit). E.g., For person entities, the age attribute should have the range as 0-150 years. 7. For attributes which are semantically similar but have different names used across different entities of the same type, merge them. E.g., Automatically identify that the attribute “birthdate” is the same as “bdate”.

analytics_vidhya icon analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

apollo_slotfilling icon apollo_slotfilling

Use the Apollo EM algorithm (a variation with multi-value variables) to solve the slot filling problem in NLP.

attentionn icon attentionn

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

aurora icon aurora

Cross-platform Beanstalk queue server console.

awesome-datascience icon awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

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