Papers and Articles talk about NLP with Deep Learning

Background Deep Learning in NLP (一)词向量和语言模型 Deep Learning in NLP (一)词向量和语言模型 CS224n: Natural Language Processing with Deep Learning Schedule and Syllabus http://web.stanford.edu/class/cs224n/syllabus.html Skip-Gram Model – Word2Vec Efficient Estimation of Word Representations in Vector Space https://arxiv.org/pdf/1301.3781.pdf Distributed Representations of Words and Phrases and their Compositionality https://arxiv.org/pdf/1310.4546.pdf Word2Vec Tutorial – The Skip-Gram…

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Using Stanford POS tagger in NLTK

Add enviroment variable first: export CLASSPATH=dir/stanford-postagger-full-2015-04-20/stanford-postagger.jar export STANFORD_MODELS=dir/stanford-postagger-full-2015-04-20/models http://stackoverflow.com/questions/13883277/stanford-parser-and-nltk/34112695#34112695 >>> from nltk.tag import StanfordPOSTagger >>> st = StanfordPOSTagger(‘english-bidirectional-distsim.tagger’) >>> st.tag(‘What is the airspeed of an unladen swallow ?’.split()) [(‘What’, ‘WP’), (‘is’, ‘VBZ’), (‘the’, ‘DT’), (‘airspeed’, ‘NN’), (‘of’, ‘IN’), (‘an’, ‘DT’), (‘unladen’, ‘JJ’), (‘swallow’, ‘VB’), (‘?’, ‘.’)]  

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B-Spline Surface Implementation with OpenGL

In this implementation, instead of using any B-Spline built-in functions of OpenGL, I made my own functions calculate points on surface base on given knots and control points. OK, let’s start! Basic Information First of all, I want to specify some notations in this implementation. Here are two core expression…

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Gem5 Basic Guideline

All contents original from https://github.com/dependablecomputinglab This article is just a backup incase myself later using. Gem5 Installation step 1: install prerequisites for Ubuntu $ sudo apt-get update $ sudo apt-get upgrade $ sudo apt-get install -y git build-essential g++ zlib1g-dev scons m4 swig python-dev step 2: Download & Build Download

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Tips for basic data analysis in Python

source from python.org file header [code language=”python”] from sklearn.decomposition import PCA from sklearn.lda import LDA from numpy import genfromtxt from sklearn import preprocessing import numpy as np [/code] load data from csv file [code language=”python”] raw_sensor = genfromtxt(‘sensor.csv’, dtype=’string’, delimiter=’,’) string_sensor = raw_sensor[1:,1:9] sensor = string_sensor.astype(np.float) X = sensor[:100, 6:8]#0:2…

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