Deepanjan Bhattacharyya's repositories
Allstate_Claims_Serverity
a kaggle competition
aws-sdk-java
The official AWS SDK for Java.
Blog-Site
Blog Website from scratch using Mongo/Mongoengine/Flask
BuildingMachineLearningSystemsWithPython
Source Code for the book Building Machine Learning Systems with Python
hello-world
Hello world repository, first hands on github
Machine-Learning
Machine Learning Projects using R, Mahout, Python and NLP
models
Models built with TensorFlow
PARALLELISING_NLP_TASKS_USING_MAP-REDUCEPARADIGM
Natural Language Processing(NLP) refers to the applications that deal with natural language in a way or other. The proposed system tries to parallelise NLP tasks using map-reduce paradigm. MapReduce is a framework for per- forming data-intensive computations in parallel on commodity computers. The main NLP tasks that the proposed system performs are POS tagging, Stemming, Stopword Removal, Named-entity recognition and Sentimental analysis. The MapReduce algorithm is implemented in Hadoop which is an open source framework for writing and running distributed applications that process large amounts of data. If tasks like POS tagging and Stemming are done sequentially, it may take a lot of time. This is where Hadoop is relevant. Hadoop scales up linearly to handle larger data sets by adding more nodes to the cluster. It also allows users to quickly write efficient parallel code. Keywords: Map-Reduce, NLP, Hadoop, POS Tagging, Stemming, Stopword Removal, Named-Entity Recognition, Sentimental Analysis
ProducerConsumer
3 Producer 1 Consumer without using semaphore.h
Something_Python
Anything coded in python