Manas Ranjan Kar manasRK

#Public Data Sources

##Legislative

IASNLP-2015 Project list

#####1. Shallow Parsers for different languages#####

Description - POS Tagging and Chunking for Gujarati,Odia, Hindi, Bengali, Marathi, Telugu (individual project for each language)
We will implement many supervised algorithms including CRF, HMM, MaxEnt, SVM, some semi-supervised classification methods, finally an unsupervised one. Will try to implement Morph Analyzer if time permits. students need to annotate data, understand the challenges, compare results given by multiple
Mentor: Pruthwik M

start new:

tmux

start new with session name:

tmux new -s myname

If you deleted a file from the working tree, then commit the deletion:

$git commit -a -m "A file was deleted"

When you do a pull request on a branch, you can continue to work on another branch and make another pull request on this other branch.

Before creating a new branch, pull the changes from upstream. Your master needs to be up to date.

Create the branch on your local machine and switch in this branch :

	#List unique values in a DataFrame column
	pd.unique(df.column_name.ravel())

	#Convert Series datatype to numeric, getting rid of any non-numeric values
	df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

	#Grab DataFrame rows where column has certain values
	valuelist = ['value1', 'value2', 'value3']
	df = df[df.column.isin(value_list)]

	import os
	import math
	import re
	import pandas as pd
	from collections import Counter
	from sklearn.datasets import fetch_20newsgroups

	#get a subset of the dataset

	categories = [

	"""Information Retrieval metrics

	Useful Resources:
	http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
	http://www.nii.ac.jp/TechReports/05-014E.pdf
	http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
	http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
	Learning to Rank for Information Retrieval (Tie-Yan Liu)
	"""
	import numpy as np

	import torch
	import torch.nn as nn
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
	import torch.nn.functional as F
	import numpy as np
	import itertools

	def flatten(l):
	return list(itertools.chain.from_iterable(l))