Sandy4321

Share Your Papers!

Here are links to influential academic papers in NLP. Learning the history is important to learn about what is going on outside of our code in the communities.

There is definitely a rich history going back all the way to even as far as 1960 - 1980's that has shaped this field into what it is.

It is also a very exciting time due to the success of machine learning off springing originally from the study of Artificial Intelligence coming to fruition as Moores has consistently tipped computation to critical mass. Natural Language Processing is also seeing similar parallels and so is a hot topic everywhere. Reading through these documents will help give you the bigger picture and where we currently stand.

	# A simple cheat sheet of Spark Dataframe syntax
	# Current for Spark 1.6.1

	# import statements
	from pyspark.sql import SQLContext
	from pyspark.sql.types import *
	from pyspark.sql.functions import *

	#creating dataframes
	df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data

	#!/usr/bin/env python3

	import array

	BYTES = 2 # N bytes arthimetic
	MAX = 2 ** (BYTES * 8 - 1) - 1
	MIN = - (2 ** (BYTES * 8 - 1)) + 1

	CHUNK= 1024

	#!/usr/bin/python
	#
	# Created by Albert Au Yeung (2010)
	#
	# An implementation of matrix factorization
	# http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
	#
	try:
	import numpy
	except:

	{

	"metadata": {

	"name": "",

	"signature": "sha256:a227026fb0076409b233b4ddec5c00bdf9e113f912da0e071cdb5dbd968c03e7"

	},

	#!/usr/bin/env python

	"""Basic Python Cheat Sheet by Filip Kral on 2015/02/16"""

	"""
	Python is a cross-platform, interpreted, object-oriented programming language.
	That means you can run it on Linux, Windows, Mac, and other platforms,
	you don't need to compile your code to execute it because it is compiled on
	the fly, and you can use classes and objects.

	### Title: Back to basics: High quality plots using base R graphics
	### An interactive tutorial for the Davis R Users Group meeting on April 24, 2015
	###
	### Date created: 20150418
	### Last updated: 20150423
	###
	### Author: Michael Koontz
	### Email: [email protected]
	### Twitter: @michaeljkoontz
	###

	## Analyze R packages for popularity, using pagerank algorithm

	# Inspired by Antonio Piccolboni, http://piccolboni.info/2012/05/essential-r-packages.html

	library(miniCRAN)
	library(igraph)
	library(magrittr)


	# Download matrix of available packages at specific date ------------------