SEMANTIC CLUSTERING OF QUESTIONS RESEARCH REPORT, 2 ND SEMESTER - PowerPoint PPT Presentation

Jul 12, 2023 •211 likes •330 views

SEMANTIC CLUSTERING OF QUESTIONS RESEARCH REPORT, 2 ND SEMESTER Cristina Groap Problem statement 2 Part of the Smart Presentation project Efficient management of audience feedback Question clustering: Suggest similar asked

SEMANTIC CLUSTERING OF QUESTIONS RESEARCH REPORT, 2 ND SEMESTER Cristina Groap ă
Problem statement 2  Part of the Smart Presentation project  Efficient management of audience feedback  Question clustering:  Suggest similar asked questions  Group all questions according to topic  Important: real-time process
Specificity 3  Specificity = Information Content  E.g. { collie, sheepdog} vs. { go, be}  Evaluation:  Taxonomy depth  Corpus-based  Combine with measures of semantic similarity for better results
Semantic Similarity Measures 4  Path-based  Leacock-Chodorow:  IC-based  Resnik:  Semantic Relatedness  Hirst-and-St.Onge:
NLP Tools 5  Stanford CoreNLP  LingPipe  Java Wordet::Similarity
Implementation 6
Results 7  143 questions ~ 8 min (dualCore 2GHz processor, 3GB RAM)  Good:  Bad:
Results (2) 8  Good and bad:
Future work 9  Test on real data  Increase weight on NERs compared to common nouns  Introduce specificity  Word Sense Disambiguation
Thank you 10  Questions?

Recommend

Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns

Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns in data, or grouping similar groups of data-points together into clusters . Clustering algorithms for numeric data: Lloyds K-means, EM

693 views • 19 slides

Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative

LUDWIG- MAXIMILIANS- INSTITUTE DATABASE UNIVERSITT FOR SYSTEMS MNCHEN MNCHEN INFORMATICS INFORMATICS GROUP GROUP Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative Clustering, Multiview

517 views • 9 slides

Evolutionary Clustering Presenter: Lei Tang Evolutionary Clustering Evolutionary Clustering

Evolutionary Clustering Presenter: Lei Tang Evolutionary Clustering Evolutionary Clustering Processing time stamped data to produce Processing time stamped data to produce a sequence of clustering. Each clustering should be similar to

432 views • 32 slides

Clustering A Categorization of Major Clustering Methods Partitioning Methods

Clustering What is Clustering? Types of Data in Cluster Analysis Clustering A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods 1 2 What is Clustering? What is Clustering? Typical

649 views • 18 slides

Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics

Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics Elhanan Borenstein A quick review The clustering problem: partition genes into distinct sets with high homogeneity and high separation Many

774 views • 43 slides

Cl Clustering t i A Categorization of Major Clustering Methods Partitioning Methods

Clustering What is Clustering? Types of Data in Cluster Analysis Cl Clustering t i A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Hierarchical Methods 1 2 What is Clustering?

617 views • 24 slides

Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and

Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein A quick review The clustering problem: partition genes into distinct sets with high homogeneity

772 views • 40 slides

CSCE 478/878 Lecture 8: Stephen Scott Clustering Introduction Outline Clustering Stephen

CSCE 478/878 Lecture 8: Clustering CSCE 478/878 Lecture 8: Stephen Scott Clustering Introduction Outline Clustering Stephen Scott k -Means Clustering Hierarchical Clustering sscott@cse.unl.edu 1 / 19 Introduction CSCE 478/878 If

201 views • 19 slides

Clustering and Dimensionality Reduction Preview Clustering K -means clustering

Clustering and Dimensionality Reduction Preview Clustering K -means clustering Mixture models Hierarchical clustering Dimensionality reduction Principal component analysis Multidimensional scaling Isomap

1.61k views • 17 slides

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering?

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering? Aykut Erdem May 2016 Hacettepe University Last time K-Means An iterative clustering algorithm - Initialize: Pick K random points as cluster

936 views • 64 slides

PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin

PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin Motivation Example Clustering cannot be analyzed without specifying what it will be used for! be used for! Example Cluster then pack

295 views • 28 slides

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering K-means Mixture of Gaussians Expectation Maximization Variational Methods 2 Clustering 3 K- means clustering What is clustering?

1.32k views • 66 slides

Trust based Clustering for Group Trust based Clustering for Group Trust based Clustering for

Trust based Clustering for Group Trust based Clustering for Group Trust based Clustering for Group Trust based Clustering for Group Key Management Key Management Key Management Key Management Hamida SEBA Graphs, Algorithms and Applications

420 views • 15 slides

Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering

Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering Clustering by Partitioning, e.g. k-Means Density Based Clustering, e.g. DBScan Grid Based Clustering Compendium slides for Guide to Intelligent

1.32k views • 82 slides

Clustering kMeans, Expectation Maximization, Self-Organizing Maps Outline K-means

Clustering kMeans, Expectation Maximization, Self-Organizing Maps Outline K-means clustering Hierarchical clustering Incremental clustering Probability-based clustering Self-Organising Maps Classification vs. Clustering

1.01k views • 67 slides

Outline Linkage-based Clustering Motivation Definitions Clustering with Semantic

Outline Linkage-based Clustering Motivation Definitions Clustering with Semantic Links Applications Algorithms SimRank Yin X., Han J. and Yu P. S., 2006, LinkClus: Efficient ReCoM Clustering via Heterogeneous

223 views • 8 slides

THE ART OF WORDS DIGITAL CLASS How to communicate your value as a (totally genius and absolutely

THE ART OF WORDS DIGITAL CLASS How to communicate your value as a (totally genius and absolutely magical) graphic designer Brought to you by Cass Lane from Wild Spirit Copywriting. WWW.WILDSPIRIT.CO Resident word-wooer and Owner of Wild

264 views • 14 slides

Search for the Higgs boson in H W W* l l decays Davide Gerbaudo Princeton

Young Scientist Forum Rencontres de MoriondEW March 2010 Search for the Higgs boson in H W W* l l decays Davide Gerbaudo Princeton University on behalf of the D collaboration Motivation Selection

386 views • 13 slides

Twisted non-Abelian vortices rpd Lukcs, in collaboration with Pter Forgcs, Fidel A.

Twisted non-Abelian vortices rpd Lukcs, in collaboration with Pter Forgcs, Fidel A. Schaposnik Wigner RCP RMKI, Budapest, Hungary Non-Perturbative Methods in Quantum Field Theory 810. October 2014 Outline Introduction

419 views • 30 slides

Meeting of the Finance Committee November 15, 2013 1 FINANCE COMMITTEE CONSENT AGENDA ITEMS 1.

Meeting of the Finance Committee November 15, 2013 1 FINANCE COMMITTEE CONSENT AGENDA ITEMS 1. Acquisition of Department of Forestry Property Located at 480 George Dean Drive, Charlottesville, Virginia 2. Amendment to the Qualified

501 views • 46 slides

Locality sensitive hashing Instructor: Sham Kakade 1 SK notes quick sort (check)

CSE 547/Stat 548: Machine Learning for Big Data Lecture Locality sensitive hashing Instructor: Sham Kakade 1 SK notes quick sort (check) physical sorting Voronoi diagram 2 The Nearest Neighbor Problem We have n points, D = {

317 views • 4 slides

TAS 3 Workshop Architecture Using ZXID and PERMIS Sampo Kellomki (sampo@symlabs.com), Symlabs

TAS 3 Workshop Architecture Using ZXID and PERMIS Sampo Kellomki (sampo@symlabs.com), Symlabs 25-26-27.8.2009 Lisboa 01 Notes from Buda IdP integration items * AMQP or SAWS * OCT support, for generat- ing tokens PEP integration items *

550 views • 35 slides

SE 1: Software Requirements Specification and Analysis Lecture 9: UML Class (Concept), Object,

SE 1: Software Requirements Specification and Analysis Lecture 9: UML Class (Concept), Object, Communication Diagrams Nancy Day, Davor Svetinovi c http://www.student.cs.uwaterloo.ca/cs445/Winter2006 uw.cs.cs445 U Waterloo SE1 (Winter

811 views • 54 slides

CS 101: Computer Programming and Utilization About These Slides Based on Chapter 18 of the

CS 101: Computer Programming and Utilization About These Slides Based on Chapter 18 of the book An Introduction to Programming Through C++ by Abhiram Ranade (Tata McGraw Hill, 2014) Original slides by Abhiram Ranade First update

483 views • 34 slides

Download document

More recommend

Explore More Topics

Stay informed with curated content and fresh updates.

animals pets art culture automotive transportation business finance computer internet construction architecture education-career electronics communication