Deep Learning Automated Helpdesk
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AUTOMATED HELPDESKFINAL YEAR PROJECT (7TH SEM)
SUBMITTED BYNIKHIL PATHANIA
PARTHA PRATIM KURMIPRANAV SHARMARISHABH KUMAR
SOURAV KUMAR PAUL
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PRESENTATION TIMELINE
Theoretical NLPKnowledge Base Design
By – Pranav Sharma
Practical NLP ApplicationForming of Tokens
By – Rishabh Kumar
Clustering
By – Sourav Kr Paul
Tensorflow
By – Nikhil Pathania
Query Model
By – Partha PratimKurmi
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PROJECT TIMELINE
Problem FormulationSep-2016
Literature SurveySept-Oct 2016
Design MethodologyNov-2016
Synchronizing Modules
Nov-2016
Basic ImplementationJan- Feb 2017
Working ModelMar-2017
Accuracy ImprovementsMar- Apr 2017
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PROBLEM STATEMENT
Automate the task of customer centers.
AIM - Build a system to answer questions like"How to recharge my mobile?" - PayTM
"How to pay my bills?" - PayTM
"Why is my refund not credited?" - Book My Show
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Training Model
1.1Raw Data
1.2NLP
1.3Preprocessing
1.4Knowledge Base
1.5Clustering
O/P
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INFORMATION RETRIEVAL
• Data Sources• FAQ's• Past forum data
• Proper data extraction model• Knowledge base
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DATA EXTRACTION MODELSWHY NLP?
• 3 steps process.• Extends with clustering.• Fast, accurate.
NLP• 4 step process.• No extension with clustering.• Smaller domain.
PATTERN MATCHING
ExampleKnowledge Base - “The CEO of IBM is Samuel Palmisano.”Query - “Who is the CEO of IBM?”Format - \Q is \A
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Training Model
1.1Raw Data
1.2NLP
1.3Preprocessing
1.4Knowledge Base
1.5Clustering
O/P
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NATURAL LANGUAGE PROCESSING
• Problem Domain – English.• Aim.• Origin - Turing Test.• Annotating the sentence.• Clouds exist on mars. => <cloud, exist, mars>• Kernel sentences, T Expressions.
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KERNEL SENTENCE, T-EXP
• Kernel Sentences.• Ternary Expressions.• <Subject, Relation, Object>
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AN EXAMPLE
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KNOWLEDGE BASE
• What is it?• What to store? Proper data structure.• Mapping to original set.• NLP Annotations, parameterized variants.
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Training Model
1.1Raw Data
1.2NLP
1.3Preprocessing
1.4Knowledge Base
O/P
1.5Clustering
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PREPROCESSING:-
• Tokenization• Stop words removal.• Stemming.• POS Tagging.
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NLTK ( NATURAL LANGUAGE TOOLKIT )
• Suite of libraries.• Python Support.• Few libraries which we will be using are :-• Lexical analysis.• Parts of speech tagger
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TOKENIZATION:-
Tokenization( Word Tokenize)• Breaking stream into meaningful elements.• Stream may or may not be a meaningful sentence.
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EXAMPLE:-
"Recharge your mobile by visiting this link"
After tokenization:-['Recharge', 'your', 'mobile', 'by', 'visiting', 'this', 'link']
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STOP WORDS :-
E.g. “is, for, the, in, etc”
Target :- REMOVE THE STOP WORDS
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STOP WORDS REMOVED BY NLTK:-
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EXAMPLE :-
FromTokenization['Recharge', 'your', 'mobile', 'by', 'visiting', 'this', 'link']
After Stop Words removal['Recharge', 'mobile', 'visiting', 'link']
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STEMMING:-
Word = Stem + AffixesExample:- playing = play(stem) + ing(affixes)
TARGET:- Removing affixes from word (called stemming)E.g. plays, playing, playful all reduced to 'play'
Library in NLTK :- PorterStemmer
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EXAMPLE :-
From Stop words removal :-['Recharge', 'mobile', 'visiting', 'link']
After Stemming :-['Recharge', 'mobile', 'visit', 'link'] // input for clustering is generated
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POS TAGGING:-
POS (part of speech) = Category of Tokens in linguistics, such as verb noun etc.
Target :- Tag the tokens with the POS with a universal format.
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EXAMPLE :-From Stemming:-['Recharge', 'mobile', 'visit', 'link']
After POS Tagging:-[('Recharge', 'NN')][('mobile', 'NN')][('visit', 'VBG')][('link', 'NN')]
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Training Model
1.1Raw Data
1.2NLP
1.3Preprocessing
1.4Knowledge Base
O/P
1.5Clustering
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Training Model
1.1Raw Data
1.2NLP
1.3Preprocessing
1.4Knowledge Base
O/P
1.5Clustering
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DOCUMENT CLUSTERING – WHAT AND WHY?• Unsupervised document organization• Automatic topic organization• Topic extraction• Fast Information retrieval and filtering
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EXAMPLES
• Web document clustering for search users.
• QA document clustering to solve common problems and questions.
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WHY K-MEANS? WHY NOT ANY HIERARCHICAL ALGO?
• Time Complexity
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CLUSTERING
• Algorithm• Find k (most dissimilar) documents • Assign them as k centroid
• Until no change• For each document
• Find the most similar cluster • Use cosine similarity fn
• Recalculate the centroid of each cluster• Stop If no document was reassigned
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K-MEANS USING JACCARD DISTANCE MEASURE• Problems in Simple K-Means Procedure.• Greedy Algorithm• Doesn't guarantee the best solution.
• JACCARD Distance Measure• Find k most dissimilar document.
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OUTPUT OF PREPROCESSING
• Possible text documents are :• Recharge mobile visit link• Recharge landline visit link• Cancel ticket process• Add money wallet
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CALCULATING TF-IDF VECTORS
• Term Frequency – Inverse Document Frequency• (Weight) Ranks the importance
• Terms frequent in Document and rare in Set• Ex: College name NITS. - name is frequent but not rare.
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TF-IDF VECTOR SPACE
Add Cancel
Recharge
landline
link mobile
money
process
ticket visit wallet
0.00 0.00 0.17 0.00 0.17 0.35 0.00 0.00 0.00 0.17 0.000.00 0.00 0.17 0.35 0.17 0.00 0.00 0.00 0.00 0.17 0.000.00 0.46 0.00 0.00 0.00 0.00 0.00 0.46 0.46 0.00 0.000.46 0.00 0.00 0.00 0.00 0.00 0.46 0.00 0.00 0.00 0.46
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SELECT K-CLUSTER ( K =3)
• Use Jaccard Distance Measure - {{0},{2},{3}}Document No (I) Document No (J) Similarity0 1 0.60 2 0.000 3 0.001 2 0.001 3 0.002 3 0.00
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AFTER FIRST ITERATION
• Assigning of documents to its most similar cluster. - {{0,1},{2},{3}}• Clusters After 1st iteration: (vecspace – centroid centers)
Add Cancel
Recharge
landline
link mobile
money
process
ticket visit wallet
0.00 0.00 0.17 0.17 0.17 0.17 0.00 0.00 0.00 0.17 0.000.00 0.46 0.00 0.00 0.00 0.00 0.00 0.46 0.46 0.00 0.000.46 0.0 0.0 0.0 0.0 0.0 0.46 0.0 0.0 0.0 0.46
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CLUSTERING OUTPUT
• { { Recharge mobile visit link, Recharge landline visit link },
{ Cancel ticket process }, { Add money wallet }}
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Training Model
1.1Raw Data
1.2NLP
1.3Preprocessing
1.4Knowledge Base
O/P
1.5Clustering
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TENSOR FLOW
• What• Why• Where
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PROGRAMMING MODEL AND BASIC CONCEPTS• Computation Graph• Nodes• Tensors• Session• Extend• Run
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COMPUTATION GRAPH
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IMPLEMENTATION
• Single Device Execution• Multi Device Execution• Cross Device Communication
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SINGLE DEVICE EXECUTION
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CROSS DEVICE COMMUNICATION
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PERFORMANCE
• Data Parallel Training• Model Parallel Training• Concurrent Step for Model Computation Pipelining
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DATA PARALLEL TRAINING
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MODEL PARALLEL AND CONCURRENT STEPS
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CLUSTERING USING TENSOR FLOW
• Training Sets• Nodes• Data flow• Feed as Input• Output
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Query Model
2.1Query
2.2NLP
2.3Preprocessing
2.4Recommendation Engine
O/P
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Query Model
2.1Query
2.2NLP
2.3Preprocessing
2.4Recommendation Engine
O/P
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Query Model
2.1Query
2.2NLP
2.3Preprocessing
2.4Recommendation Engine
O/P
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Query Model
2.1Query
2.2NLP
2.3Preprocessing
2.4Recommendation Engine
O/P
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RECOMMENDATION ENGINE
• Recommendation Engine analyzes available data to answer the questions• The various steps are:1. Data collection2. Preprocessing and Transformations3. Classifier Ensemble
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PREPROCESSING AND TRANSFORMATIONS
• The training set is taken consisting of FAQs, past forums etc.• Given a question, we want to deduce its genre from the texts • Only the text of the question is extracted. • Feature selection to evaluate the importance of a word using
TF-IDF
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PREPROCESSING AND TRANSFORMATIONS
• Training set derived from the key parts of speech in each sentenceExample How to recharge my mobile
Part of Speech
Verb Noun Object
Decision label Task Electronics
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PREPROCESSING AND TRANSFORMATIONS
• recharge mobile
• Find TF-IDF vector• Compare it with distinct clusters using cosine similarity
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CLASSIFIER ENSEMBLE
• Ensemble modelling is used for classification using three classifiers• Naïve Bayesian using FAQ training set• POS Naïve Bayesian• Threshold Biasing classifier
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ENSEMBLE STRUCTURE
• Learning algorithm that uses multiple classifiers• Classify using a weighted vote for their decisions• The classifier having better precision is considered
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RESULTS
• Documents are hand-tagged with the genres• In the Ensemble approach, we use a bag approach• The count of genres is taken into account• The top tallied genre is used to generate result• Answer is "recharge mobile visit link"
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Query Model
2.1Query
2.2NLP
2.3Preprocessing
2.4Recommendation Engine
O/P
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INNOVATION• Sections Removed• User friendly• Reduced Man-power• Future plans to collaborate with college website.
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CONCLUSION AND OUTCOMESThe outcomes of this project can be formulated (but not limited to) in the following points :-1. Complete Designed Architecture.2. Proper modules and uses defined.3. Model solution to the problem.
Hence we would like to conclude that the theoretical and survey aspect of the problem is complete. We have selected the best tech solutions after surveying for all existing alternatives. Thus, a working model is soon to be expected from the team.
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LITERATURE SURVEYSerial No
Paper Title Authors
1 Natural Language Annotations for Question Answering
Boris Katz, Gary Borchardt and Sue Felshin
2 Using English for Indexing and Retrieving Katz, Boris3 Recommendation engine: Matching
individual/group profiles for better shopping experience
Sanjeev Kulkarni, Ashok M. Sanpal, Ravindra R. Mudholkar, kiran Kumari
4 Recommendation engine for Reddit Hoang Nguyen, Rachel Richards, C.C. Chan, Kathy J. Liszka
5 TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Mart´ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo
6 Executing a programon the MIT tagged-token dataflow architecture.IEEE Trans. Comput., 1990.
Arvind and Rishiyur S. Nikhil
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LITERATURE SURVEYSerial No
Paper Title Author
7 An efficient K-Means Algorithm integrated with Jaccard Distance Measure for Document Clustering
Mushfeq-Us-Saleheen Shameem, Raihana Ferdous
8 An Intelligent Similarity Measure for Effective TextDocument Clustering
M.L.AISHWARYA1Department of Computer Science , K.SELVI2
9 K Means Clustering with Tf-idf Weights Jonathan Zong10 Comparison Between K-Mean and Hierarchical
AlgorithmUsing Query Redirection
Manpreet kaur , Usvir Kaur
11 Question Answering System on Education Acts Using NLP Techniques
Dr.M.M. Raghuwanshi Professor , Department Of Computer Science and Technology
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LITERATURE SURVEYSerial No
Paper Title Author
12 Affective – Hierarchical Classification of Text – An Approach Using NLP Toolkit
Dr.R.Venkatesan Asst.Prof-III/CSE
13 Building high-level features using large scale unsupervisedlearning. In ICML’2012, 2012.
Quoc Le, Marc’Aurelio Ranzato, Rajat Monga, and AndrewNg.
14 Preprocessing Techniques for Text Mining - An Overview
Dr. S. Vijayarani1, Ms. J. Ilamathi, Ms. Nithya, Assistant Professor, M. Phil Research Scholar,Department of Computer Science
15 Annotating the World Wide Web using Natural Language
Boris Katz
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THANK YOU !!