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人工智能资料库:第7辑(20170111)

AllenOR灵感
 AllenOR灵感
发布于 2017/09/10 01:24
字数 631
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  1. 【论文&代码】emoji2vec: Learning Emoji Representations from their Description

简介:

这是一篇从表情生成向量的论文,本人在情感分析中使用,得到了很好的效果。你可以仔细研究一下这篇文章。

Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emojis which are learned from their description in the Unicode emoji standard.1 The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream
task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emojis need to appear
frequently in order to estimate a representation.

原文链接:https://arxiv.org/pdf/1609.08359.pdf

代码链接:https://github.com/uclmr/emoji2vec


2.【博客】iSee: Using deep learning to remove eyeglasses from faces

简介:


利用卷积神经网络去除图片中的眼镜。

原文链接:https://blog.insightdatascience.com/isee-removing-eyeglasses-from-faces-using-deep-learning-d4e7d935376f#.2fl6cotoh


3.【代码】Deep Learning for Natural Language Processing

简介:

deepnl is a Python library for Natural Language Processing tasks based on a Deep Learning neural network architecture. The library currently provides tools for performing part-of-speech tagging, Named Entity tagging and Semantic Role Labeling. deepnl also provides code for creating word embeddings from text, using either the Language Model approach by [Collobert11], or Hellinger PCA, as in [Lebret14].It can also create sentiment specific word embeddings from a corpus of annotated Tweets.If you use deepnl, please cite [Attardi] in your publications.

代码链接:https://github.com/isabelleaugenstein/deepnl


4.【视频】How to Install OpenAI's Universe and Make a Game Bot [LIVE]

简介:

这是我比较喜欢的一个频道,这人教学很有趣。如果你喜欢,可以在YouTube上面订阅他。我也会选几个比较好玩或者有用的视频,分享给大家。

I'm going to go through the steps necessary to install OpenAI's Universe, then we'll build our own game bot using reinforcement learning. This code will be in Python.

原文链接:https://www.youtube.com/watch?v=XI-I9i_GzIw


5.【NLP学习课程】An interactive Statistical NLP book in Python

简介:

有笔记,有PPT,有联系,有代码。非常好的学习NLP的教材。

Welcome to this interactive book on Statistical Natural Language Processing (NLP). NLP is a field that lies in the intersection of Computer Science, Artificial Intelligence (AI) and Linguistics with the goal to enable computers to solve tasks that require natural language understanding and/or generation. Such tasks are omnipresent in most of our day-to-day life: think of Machine Translation, Automatic Question Answering or even basic Search. All these tasks require the computer to process language in one way or another. But even if you ignore these practical applications, many people consider language to be at the heart of human intelligence, and this makes NLP (and it's more linguistically motivated cousin, Computational Linguistics), important for its role in AI alone.

原文链接:http://nbviewer.jupyter.org/github/uclmr/stat-nlp-book/blob/python/overview.ipynb


本文转载自:http://www.jianshu.com/p/c34e67fbd20b

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