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Large Scale Hierarchical Text Classification
 

Large Scale Hierarchical Text Classification

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Large Scale Hierarchical Text Classification using Hadoop:MapReduce

Large Scale Hierarchical Text Classification using Hadoop:MapReduce

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    Large Scale Hierarchical Text Classification Large Scale Hierarchical Text Classification Presentation Transcript

    • Large Scale Hierarchical Text Classification using Hadoop:MapReduce Hammad Haleem (10-css-25) Pankaj Kumar sharma (10-css-46) Department of Computer Engineering Jamia Millia Islamia University New Delhi India
    • S.no. Topic 1 Objective 2 Hierarchical Text Classification (HTC) and dataset 3 About Our Technique 5 About Hadoop and Mapreduce 6 Progress Reporting 7 References Table of Contents
    • OBJECTIVE To develop and deploy a cost-effective, near real time and Distributed Hierarchical Text Documents’ classification System which could be used to classify documents in a huge Hierarchy of Categories in real time.
    • What is Hierarchical Text Classification ? ▪ Documents are said to follow a hierarchical classification if: – If a single document is present in more than one category. – And the categories are themselves in a hierarchy. So a single category can contain multiple documents and even multiple categories.
    • Reuters Corpus (RCV1) • Raw data from news articles. • Approximately 806,791 documents. It has 1000+ categories. Wikipedia Dataset • The data is in the SVM format and requires a very less amount of preprocessing. • The dataset has 2,400,000 documents in 325,000 categories Used dataset
    • OUR APPROACH -DETAILED DESCRIPTION OF ALGORITHM We divided the development time into two phases. 1. Where we did the initial environment setup and wrote functions which will help in further development. 2. We used these functions to perform the training and testing on actual data. The section ahead talks in detail about the various steps performed at various phases of project.
    • WE DEVELOPED FOLLOWING FUNCTIONS : INITIAL PHASE These methods are quite frequently used in the project so we would like to discuss them ● Train (TrainingDocumentSet D, CategoryTree C) ○ TrainingDocumentSet D is the set of training documents (i.e., already marked with their corresponding categories). ○ CategoryTree C will contain a list of Categories, the parent-child relation between them. ● Classify (Document d, CategoryTree C, TainedClassifier TC) ○ Document d would be the document to be classified. ○ CategoryTree C will contain a list of categories, the hierarchical parent-child relation between them. ○ TrainedClassifier TC is the output of Train API call ● Tf-Idf-Calculator (DocumentSet D) ○ DocumentSet D is the set of Text Documents for which we’ve to calculate Tf-Idf weights. ● CosineDistance(Document d1, Document d2) ○ will return the cosine distance between documents d1 and d2.
    • HIGH LEVEL VIEW OF THE WHOLE CLASSIFICATION ALGORITHM.
    • DEEP HIERARCHICAL CLASSIFICATION : TRAINING PHASE ▪ G Following diagram gives an overview of the training technique. ▪ The training follows lazy learner technique and is quite efficient for the problem.
    • DEEP HIERARCHICAL CLASSIFICATION : TRAINING PHASE The DCLTH is a Lazy learning algorithm, therefore the training phase is quite straight forward. First of all, all training documents undergo preprocessing. In this layer mostly following activities are performed: 1. Removal of HTML tags or other noisy data from the documents. 2. Converting special documents like docs, pdfs to simple word based format. 3. Stemming words, i.e., converting words to their corresponding root words. (We’ll use Potter Stemming). 4. Stop word removal. i.e., removing common words like “a”, “the”, etc. 5. Documents are represented in libSVM format.
    • DEEP HIERARCHICAL CLASSIFICATION : TRAINING PHASE After preprocessing is done, the document set undergo Tf-Idf calculator and the Tf-Idf value for each word related to each document is found.We would use following definition of Tf, Idf and Tf-Tdf: Tf(w,d) = f(w, d)/sum(d) Where f(w, d) is frequency of w, sum(d) is sum of frequencies of each unique word. Idf(w,d,D) = log( N / (1+n)) Where N is number of documents in set D, and n is number of documents in which W appears. 1 is added to avoid “Division by 0 error”. Finally, Tf-Idf(w,d,D) = Tf(w, d)*Idf(w,d,D). Finally this information is stored for later usage. This completes the training phase of DCLTH.
    • DEEP HIERARCHICAL CLASSIFICATION : TESTING PHASE ❏ The novel Deep Classification in Hierarchical Text Classification performs better from other classification algorithms because instead of applying classification directly over all the categories, it first divides the categories into two categories, namely related and unrelated categories and then using the hierarchical information related to the category it employes top-down approach to classify the document. ❏ Generally we have two phases for the testing process.
    • DEEP HIERARCHICAL CLASSIFICATION : TESTING PHASE LEVEL 1 The aim of this stage is to divide the large category set into two subset, namely “related” and “unrelated”. For any document d, the number of related categories is much smaller than the number of unrelated categories.The point worth noting is that now from a huge set of categories we have only a small number of categories left on which the real classification could take place.This phase works as follow: 1. The given document is processed (in the similar fashion as explained in “Training” stage). 2. Processed document is then used to find k most similar documents. For this we would employ the Cosine distance and k NN algorithm. 3. From these k documents we get the set of related categories (called candidate categories).
    • DEEP HIERARCHICAL CLASSIFICATION : TESTING PHASE LEVEL2 The aim of second stage is to work upon the candidate categories and processed document (both obtained from first stage) and to obtain most probable categories from them. This level would work as follow: 1. From the given candidate categories, a pruned tree is formed. An example of such a tree is given in the following diagram. 2. From this pruned tree, we start from the root and apply Standard Naive Bayes’ Classifier to further prune down the tree. 3. Algorithm stops when we reach the bottom most level. 4. Categories related to the finally pruned tree (called final category is pruned).
    • DID WE SAY “DISTRIBUTED” ? ❏ Most of the techniques, which lie under the above two approaches of HTC are Linear and runs are tested on a Single System, not on any cluster. ❏ This project aims to : ❏ Deploy any one of these popular approaches to run parallelly on Hadoop cluster, ❏ Develop our own approach optimised to perform best parallely.
    • Distributed Processing and Hadoop ▪ The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. ▪ It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ▪ Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
    • More from our Hadoop Cluster. 1. We showcase some screenshots from the hadoop cluster in next few slides. a. From various panels of hadoop platform i. Node Manager GUI ii. DFS gui iii. Job Tracker GUI
    • What is Mapreduce and How Hadoop helped us ? ▪ MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster ▪ The model was initially – "Map" step: The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node. – "Reduce" step: The master node then collects the answers to all the subproblems and combines them in some way to form the output – the answer to the problem it was originally trying to solve.
    • PROGRESS REPORT ▪ Setup of Cluster ▪ Analysis of various algorithms for Training and testing ▪ Training of the dataset. ▪ Testing of dataset. ▪ Implementing the Whole classifier without Mapreduce. ▪ Implementing the Classifier with mapreduce.
    • References 1. www.kaggle.com/c/lshtc [Dataset and Problem description] 2. Distributed Hierarchical text classification framework, US Patent US 7809723 B2 3. www.about.reuters.com/researchandstandards/corpus 4. www.hadoop.apache.org [Hadoop docs] 5. Hadoop: The Definitive Guide. 6. DKPro TC http://code.google.com/p/dkpro-tc/ 7. RTextTools https://github.com/timjurka/RTextTools 8. DigitalPebble TC https://github.com/DigitalPebble/TextClassification 9. Some other techniques in addition to DCLTH are: 10. Dumais, Susan, and Hao Chen. "Hierarchical classification of Web content." Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2000. 11. Granitzer, Michael. "Hierarchical text classification using methods from machine learning." Master's Thesis, Graz University of Technology (2003).