This page assembles different topics to which my work is related on. It is composed of two main parts, Information Access and Machine Learning.
Abe N., Mamitsuka H.
Query learning strategies using boosting and bagging
ICML 1998
PDF
Campbell C., Cristianini N., Smola A.
Query Learning with Large Margin Classifiers.
ICML 2000
PDF
Cohn D.A., Atlas L., Ladner R.
Improving Generalization with Active Learning.
ML 92
PDF
Cohn D.A., Ghahramani Z., Jordan M.I.
Active Learning with Statistical Models.
NIPS 96
Postscript
Dagan I., Engelson S.P.
Committee-Based Sampling for Training Probabilistic Classifiers.
ICML 1995
PDF
Dasgupta S.
Analysis of a greedy active learning strategy.
NIPS 2004
PDF
Dasgupta S.
Coarse sample complexity bounds for active learning.
NIPS 2005
PDF
Freund Y., Seung H.-S., Shamir E., Tishby N.
Selective Sampling Using the Query by Committee Algorithm.
ML 1997
PDF
Lewis D., Gale W.
A sequential Algorithm for Training Text Classifiers
SIGIR 1994
PDF
Long P.M.
Minimum Majority Classification and Boosting
AAAI 2002
PDF
Muslea I., Minton S., Knoblock C.A.
Active+Semi-Supervised Learning = Robust Multi-View Learning.
ICML 2002
PDF
Roy N., McCallum A. K.
Toward optimal Active Learning through Sampling Estimation of Error Reduction.
IJCAI'99
PDF
Schohn G., Cohn D.
Less is more: Active learning with support vector machines.
ICML 2000
Postscript
Seong-Bae P., Zhang B.-T.
Document Filtering Boosted by Unlabeled Data.
IEEE International Symposium on Industrial Electronics 2001
PDF
Seung H.S., Opper M., Sompolinsky H.
Query by Committee.
Proceedings of the Fifth Workshop on Computational Learning 1992
PDF
Sung K.K., Niyagi P.
Active Learning for Function Approximation.
NIPS'95
PDF
Tong S., Koller D.
Support Vector Machine Active Learning with Applications to Text Classification.
ICML 2K
PDF
Tong S.
Active Learning: Theory and Applications.
Ph.D. 2001
PDF
Vlachos A.
Active Learning with Support vector machines.
Master Thesis, 2004
PDF
Bauer E., Kohavi R.
An Empirical Comaprison of Voting Classification Algorithms: Bagging, Boosting and variants.
Machine Learning
Postscript
Breiman L.
Bagging Predictors.
Machine Learning
Postscript
Friedman J., Hastie T., Tibshirani R.
Additive Logistic Regression: a Statistical View of Boosting
Technical Report 1998
PDF Postscript
Grove A.J., Schuurmans D.
Boosting in the limit: Maximizing the margin of the learned ensembles.
AAAI 98
PDF
Iyer R.D.
An Efficient Boosting Algorithm for Combining Preferences.
Master Thesis 99
PDF
Laferty J.
Additive Models, Boosting and Inference for Generalized Divergences
COLT 99
Postscript
Lebanon G., Lafferty J.
Boosting and Maximum Likelihood for Exponential Models
Technical Report 2001
PDF
Mason L., Baxter J., Bartlett P.L. Frean M.
Functional Gradient Techniques for Combining Hypotheses
In Advances in Large Margin Classifiers, Eds. Smola, Bartlett,
Schölkopf and Schuurmans 1999
Postscript
Schapire R.E., Freund Y., Bartlett P., Sun Lee W.
Boosting the Margin: A new explanation for the Effectiveness of Voting Methods.
The Annals of Statistics 1998
Postscript
Schapire R.E.
The Strenght of Weak Learnability.
ML 1999
PDF
Schapire R.E.
Theoretical views of Boosting.
EuroColt 1999
Postscript
Schapire R.E., Singer Y.
Improved Boosting Algorithms Using Confidence-reted
Predictions.
Machine Learning 1999
PDF
Blimes J.A.
A Gentle Tutorial of the EM algorithm and its application to the parameter estimation for gaussian mixture and Hidden Markov Models.
Tutorial 1998
PDF
Cadez I., Gaffney S., Smyth P.
A General Probabilistic Framework for Clustering Individuals and Objects.
KDD 2000
PDF
Ding C., He X.
Cluster merging and splitting in hierarchical clustering algorithms.
ICDL'01
PS
El-Yaniv R., Souroujon O.
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
ECML 2001
PS
Fraley C., Raftery A.E.
How many clusters? Which Clustering Method? Answers via Model-Based Cluster Analysis
Technical Report
PDF
Govaert G., Nadif M.
Clustering with block mixture models.
Pattern Recognition 2003
PS
Gondek D., Hofmann T.
Conditional Information Bottleneck Clustering.
IEEE Data Mining 2003
PDF
Haralick, R. Harpaz R.
Linear Manifold Clustering
MLDM 2005
PDF
Pelleg D., Moore A.,
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML'2K
PDF
Slonim N., Tishby N.
Document Clustering using Word Clusters via the Information Bottleneck Method
Research and Developpment in Information Retrieval 2000
PS
Xing E.P., Ng A.Y., Jordan M.I., Ruseell S.
Distance Metric Learning, with Application to Clustering with Side-Information
NIPS 15 - 2002
PDF
Collins M., Dasgupta S., Schapire R.E.
A Generalization of Principal Component Analysis to the Exponential Family
NIPS 2001
PS
Dasgupta S.
Experiments with Random Projection.
UAI 2000
PDF
Miasnikov A.D., Rome J.E., Haralick R.M.
A Hierarchical Projection Pursuit Clustering Algorithm
ICPR 2004
PDF
Hastie T., Tibshirani R., Buja A.,
Flexible Discriminant Analysis by Optimal Scoring.
Journal of the American Statistical Association 1993
PDF
Hastie T., Tibshirani R., Buja A.,
Flexible Discriminant and Mixture Models.
Neural Networks and Statistics 1995
PDF
Hastie T., Buja A., Tibshirani R.
Penalized Discriminant Analysis.
Annals of Statistics, 1995
Postscript
Agarwal S., Roth D.
Learnability of Bipartite Ranking Functions
COLT, 2005
PDF
Bartlett P.L., Bousquet O., Mendelson S.
Local Rademacher Complexities
Annal of Statistics, 2005
PDF
Blanchard G., Bousquet O., Massart P.
Statistical Performance of Support Vector Machines
Annal of Statistics, 2004
Postscript
Bottou L.
Une Approche théorique de l'Apprentissage Connexionniste: Applications à la Reconnaissance de la Parole
PhD Theis, 1991
PDF
Bousquet O., Boucheron S., Lugosi G.
Theory of Classification: A Survey of Recent Advances
ESAIM 2005
PDF
Bousquet O., Boucheron S., Lugosi G.
Introduction to Statistical Learning Theory
Advanced Lectures on Machine Learning 2004
PDF
Cortes C.
Prediciton of Generalization Ability in Learning Machines
PhD Theis, 1995
PDF
Clémençon S., Lugosi G., Vayatis N.
Ranking and Scoring Using Empirical Risk Minimization
COLT 2005
PDF
Crammer K., Singer Y.
Loss Bounds for Online Category Ranking
COLT 2005
PDF
Kääriäinen M.
Generalization Error Bounds Using Unlabeled Data
COLT 2005
PDF
Kääriäinen M., Langford J.
A comparison of Tight Generalization Error Bounds
ICML 2005
PDF
Langford J.
Tutorial on Practical Prediction Theory for Classification
JMLR 2005
PDF
Langford J. and Seeger M.
Bounds for Averaging Classifiers
Technical Report 2001
PDF
Petra P.
Data-Dependent Analysis for Learning Algorithms
PDF
Rennie J.D.M.
Bounded Loss Classification
PDF
Rudin C., Cortes C., Mohri M., Schapire R.E.
Margin-Based Ranking Meets Boosting in the Middle
COLT 2005
PDF
Chen S., Rosenfeld R.
A Survey of Smoothing Techniques for ME Models
IEEE Transactions on speech and Audio Proceesing, 8(1)
PDF
Jaakkola T., Meila M., Jebara T.
Maximum Entropy Discrimination.
MIT AITR-1668 1999
Postscript
Freund Y., Schapire R.E.
Large margin classification using the perceptron algorithm
Machine Learning Journal, 37(3):277-296, 1999
PDF
Blei B.M., Jordan M.I.
Modeling Annotated Data
Sigir 2003
PDF
Hofmann T.
Probabilistic Latent Semantic Indexing.
SIGIR 99
PDF
Agarwal S.
Ranking on Graph Data
ICML 2006
PDF
Agarwal S., Graepel T., Herbrich R., Har-Peled S., Roth D.
Generalization Bounds for the Area Under the ROC Curve
JMLR 2005
PDF
Brinker K., Fürnkranz J., Hüllermeier E.
Label Ranking by Learning Pairwise Preferences
JMLR 2006
PDF
Brinker K.
Active Learning of Label Ranking Functions
ICML 2004
PDF
Chu W., Ghahramani Z.
Preference Learning with Gaussian Processes
ICML 2005
PDF
Cohen W.W., Scahpire R.E., Signer Y.
Learning to Order Things
NIPS 1998
PDF
Cortes C., Mohri M.
Confidence Intervals for the Area Under the ROC Curve
NIPS 2004
Postscript
Collins M.
Ranking Algorithms for Named-Entity Extraction: Boosting and Voted Perceptron
ACL 2002
PDF
Dekel O., Manning C.D., Singer Y.
Log-Linear Models for Label Ranking
NIPS 2003
PDF
Freund Y., Iyer R., Schapire R.E., Singer Y.
An Efficient Boosting Algorithm for Combining Preferences
JMLR 2003
PDF
Fürnkranz J., Hüllermeier E
Pairwise Preference Learning and Ranking
ECML 2003
PDF
Fürnkranz J., Hüllermeier E
Preference Learning
KIJ 2005
PDF
He J., Li M., Zhang H.-J., Tong H., Zhang C.
Manifold-Ranking Based Image Retrieval
MM 2004
PDF
Joachims T.
Optimizing Search Engines using ClickThrough Data
KDD 2002
PDF
Lovasz L.
Random Walk on Graphs: A Survey
Combinatorics
PS
Rudin C.
Ranking with a P-Norm Push
COLT 2006
PDF
Saar-Tsechansky M. Provost F.
Active Learning for Class Probability Estimation and Ranking
IJCAI 2001
PDF
Rudin C., Joshi A.K.
Ranking and Reranking with Perceptron
RNLP 2004
PDF
Yu H.
SVM Selective Sampling for Ranking with Application to Data Retrieval
KDD 2005
PDF
Zhou D.,Weston J.,Gretton A., Bousquet O., Schölkopf
Ranking on data Manifolds.
ICML 2004
PDF
Altun Y., McAllester D., Belkin M.
Maximum Margin Semi-supervised Learning for Structures Variables
NIPS 2005
Postscript
Ando R.K., Zhang T.
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
JMLR 2005
PDF
Balcan M.-F., Blum A.
A PAC-style Model for Learning from Labeled and Unlabeled Data
COLT 2005
PDF
Baluja S.
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data.
NIPS 93
PDF
Basu S., Banerjee A., Mooney R.
Semi-Supervised Clustering by Seeding.
ICML'02
PDF
Belkin M., Niyogi P.
Semi-Supervised Learning on Riemannian Manifolds.
Machine Learning 2004
PDF
Bennett K.P., Demiriz A. Maclin R.
Exploiting Unlabeled Data in Ensemble Methods.
KDD'02
PDF
Blum A., Mitchell T.
Combing Labeled and Unlabeled Data with Co-Training.
Colt'98
Postscript
Chen K., Wang S.
Regularizaed Boost for Semi-supervised Learning.
NIPS'07
PDF
Chapelle O., Zien A.
Semi-supervised Classification by Low Density Separation.
AI & Statistics 2005
PDF
Chapelle O., Weston J., Schölkopf B.
Cluster Kernels for Semi-Supervised Learning.
NIPS 2003
Postscript
Collins M., Singer Y.
Unsupervised Models for Named Entity Classification.
EMNLP'99
Postscript
De Comité F., Denis F., Gilleron R., Letouzey F.
Positive and unlabeled data help learning.
COLT'99
Postscript
Cozman F.G., Cohen I.
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers.
Report 2002
PDF
Cozman F.G., Cohen I., Cirelo M.C.
Semi-supervised learning of mixture models
ICML 2003
PDF
Goldman S.A., Kwek S.S., Scott S.D.
Learning From Examples With Unspecified Attribute Values.
CL'97
Postscript
Goldman S.A., Zhou Y.
Enhancing Supervised Learning with Unlabeled Data.
ICML'2K
Postscript
Grandvalet Y., Bengio Y.
Semi-supervised Learning by Entropy Minimization
NIPS 2004
PDF
Jaakkola T., Meila M., Jebara T.
Maximum entropy discrimination.
NIPS 2000
PDF
Joachims T.
Transductive Inference for Text Classification using Support Vector Machines
ICML 1999
Postscript
Lewis D., Catlett J.
Heterogenous uncertainty sampling for supervised learning
ICML 1994
PDF
Leslie C.S., Eskin E., Cohen A., Weston J., Noble W.S.
Mismatch string kernels for discriminative protein classification.
Bioinformatics 2004
PDF
Mitchell T.M.
The role of Unlabeled Data in Supervised learning.
Science Cognitive'99
Postscript
Muslea I., Minton S., Knoblock C.A.
Active+Semi-Supervised Learning = Robust Multi-View Learning.
ICML 2002
PDF
Nigam K.
Using Unlabeled Data to Improve Text Classification
Ph.D. 2001
PDF
Nigam K., Ghani R.
Analyzing the effectiveness and applicability of co-training
CIKM'2K
PDF
Nigam K., McCallum A. K., Thrum S., Mitchell T.
Text Classification from Labeled and Unlabeled Documents using EM.
Machine Learning'2K
Postscript
Ratsaby J., Venkatesh S.S.
Learning from a Mixture of Labeled and Unlabeled Examples with Parametric Side Information
ICML 1995
PDF
Ratsaby J., Maiorov V.
On the Value of Partial Information for Learning from Examples
Journal of Complexity 1998
PDF
Seeger M.
Learning with Labeled and Unlabeled Data.
Rapport 2000
Postscript
Shahshahani B.M., Langrebe D.A.
The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon.
IEEE Geoscience & Remote Sensing'94
PDF
Szummer M., Jaakkola T.
Kernel expansions with unlabeled examples.
NIPS 2000
PDF
Szummer M., Jaakkola T.
Partially labeled classification with Markov random talks.
NIPS 2001
PDF
Tur G., Hakkani-Tür D., Schapire R.E.
Combining Active And Semi-Supervised Learning for Spoken Language Understanding.
Speech Communication 2005
PDF
Zhang T.
The Value of Unlabeled Data for Classification Problems.
ICML 2000
PDF
Zhu X.,Ghahramani Z.,Lafferty J.
Semi-supervised learning using gaussian fields and harmonic fucntions.
ICML 2003
PDF
Farquhar J.D.R., Hardoon D.R., Meng H., Sahwe-Taylor J., Szedmak S.
Two view learning: SVM-2K, Theory and practice
NIPS 2003
PDF
Platt J.C.
Fast Training of Support Vector Machines using Sequential Minimal Optimization
Book Chapter
PDF
Schölkopf B., Smola A.
A Tutorial Introduction
Learning with Kernels
PDF
Sahwe-Taylor J., Szedmak S.
Synthesis of Maximum Margin and Multiview Learning using Unlabeled Data
ESANN 2006
PDF
De sa V.
Learning Classification with Unlabeled Data
NIPS'93
Postscript
De sa V.
Unsupervised Classification Learning from Cross-Modal Environmental Structure
Thesis 94
Postscript
Hinton G. H., Dayan P., Frey B. J., Neal R. M.
The wake-sleep algorithm for unsupervised neural networks.
Science 95
PDF
Jordan M.
The wake-sleep algorithm for unsupervised neural networks.
Neural Computation 94
PDF
Jordan M., Xu L.
On Convergence Properties of the EM Algorithm for Gaussian Mixtures
Neural Computation 96
PDF
Amari S.-I.
Information Geometry of the EM and em algorithms for Neural Networks.
Neural Networks'95
Postscript
Berger A.
Information Retrieval and Information Theory.
Research and Development in Information Retrieval, 1999
PDF
Berger A.
The Improved Iterative Scaling Algorithm: A Gentle Introduction
Technical Report, 1997
PDF
Boyd S.
Convex Optimisation.
Book 2004
PDF
Breiman L.
Models and Selection Criteria for Regression and Classification
Technical Report MSR-TR-97-08, Microsoft Research
Postscript
Chapelle O.
Support Vector Machines: principe d'induction, règlage automatique et connaissances a priori.
These
Postscript
Neal R.M., Hinton G.E.
A view of the EM algorithm that justifies incremental, sparse, and other variants.
Learning in Graphical Models, 355-368
PDF
Ng A., Jordan M.
On Discriminative vs. Generative classifiers: A comparison of logistic regression and Naive-Bayes.
NIPS 2001
PDF
Jaakkola T., Haussler D.
Exploiting generative models in discriminative classifiers.
NIPS 11
Postscript
Ng S.K., McLachlan G.J.
On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures
Statistics and computing 13, 2003, 45-55
PDF
McLachan G.J., Peel D.
Mixture of Factor Analyers
ICML 2000, 599-606
PDF
Meir R., El-Yaniv R., Ben-David S.
Localized Boosting.
CL'2K
Postscript
Rabiner L.
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.
IEEE 1989
PDF
Cardie C.
Empirical Methods in Information Extraction.
AI Magazine 2005
PDF
Choi Y., Cardie C., Riloff E., Patwardhan S.
Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns.
EMNLP 2005
PDF
Freitag D.
Information Extraction in HTML: Application of a General Machine Learning Approach.
AAAI 1998
Postscript
Kristjansson T., Culotta A., Viola P., McCallum A.
Interactive Information Extraction with Constrained Random Fields.
AAAI'04
PDF
Pierce D., Cardie C.
User-Oriented Machine Learning Strategies for Information Extraction: Putting the Human Back in the Loop.
IJCAI'01 Workshop on Adaptive Text Extraction and Mining
PDF
Riloff E.
Automatically Generating Extraction Patterns from Untagged Text
AAAI 96
PDF
Buckley C., Allan J., Salton Gerard
Automatic Routing and Ad-hoc Retrieval Using SMART : TREC 2.
TREC-2 1993
Postscript
Cai L., Hofmann T.
Text Categorization by Boosting Automatically Extracted Concepts.
SIGIR 2003
PDF
Cline M.
Utilizing HTML Structure and Linked Pages to Improve Learning for Text Categorization.
These 99
Postscript
Dhillon I. S., Fan J. and Guan Y.
Efficient Clusterting of very large document collections.
Chapitre de livre
PDF
Douglas Baker L.
Distributional Clustering of Words for Text Classification.
SIGIR 1998
Postscript
Fabio C.
Is This document relevant? ... probably.
ACM Computing surveys 30, 4 (Dec. 1998), 528--552
PDF
Joachims T.
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML 1997
PDF
Joachims T.
Text Categorization with Support Vector Machines: Learning with Many Relevant Features
ECML 98
PDF
Lewis David D.
An evaluation of phrasal and clustered representations on a text categorization tesk.
SIGIR 1992
PDF
Lewis David D.
Feature Selection and Feature Extraction for Text Categorization.
Proceedings of Speech and Natural Language Workshop
DARPA 1992
Postscript
McCallum A.K.
Multi-Label Text Classification with a Mixture Model Trained by EM.
AAAI'99
Postscript
McCallum A., Rosenfeld R., Mitchell T., Ng. A.
Improving Text Classification by Shrinkage in a Hierarchy of Classes.
ICML 1998
Postscript
McCallum A., Nigam. K.
Employing EM and Pool-based Active Learning for Text Classification.
ICML 1998
PDF
Mladenic D.
Feature subset selection in text-learning.
ECML 1998
PDF
Nigam K., Lafferty J., McCallum A.
Using Maximum Entropy for Text Classification.
1999
PDF
Robertson S. E., Walker S., Jones S., Hancock-Beaulieu M.-M., Gatford M.
Okapi at TREC 3.
1996
PDF
Yang Y., Pederson J. O.
A Comparative Study on Feature Selection in Text Classification
1997
PDF
Yang Y.
An Evaluation of Statistical Approaches to Text Categorization
IR 1999
PDF
Zelikovitz S., Hirsh H.
Improving Short-Text Classification Using Unlabeled BAckground Knowledge to Assess Document Similarity
ICML'2K
Postscript
Kraaij W., Spitters M., Van Der Heijden M.
Combining a Mixture Langage Model and Naive Bayes multi-document Suumarization.
DUC 2001
PDF
Westerveld T., Kraaij W., Hiemstra D.
Retrieving Web Pages using Content, Links, URLs and Anchors
TREC 10
PDF
Zhai C., Lafferty J.
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval
SIGIR 2001
PDF
Callan J. P.
Passage-Level Evidence in Document Retrieval.
SIGIR 1994
Postscript
Knaus D., Mittendorf E., Schäuble P.
Improving a Basic Retrieval Method by Links and Passage Level Evidence.
TREC-3, 1994
Postscript
Li H., Yamanishi K.
Topic Analysis using a Finite Mixture Model.
SIGADT 2000
PDF
Mittendorf E., Schäuble P.
Document and Passage Retrieval Based on Hidden Markov Model.
SIGIR 1994
Postscript
Moffat A., Sacks-Davis R., Wilkinson R. and Zobel J.
Retrieval of Partial Documents.
TREC-2 1993
Postscript
Salton G.J., Buckley C.
Automatic Text Structuring and Retrieval - Experiments in Automatic Encyclopedia searching.
SIGIR 91
PDF
Salton G.J., Allan J., Buckley C.
Approaches to Passage Retrieval in Full Text Information Systems.
SIGIR 93
PDF
Wilkinson R.
Effective Retrieval of Structured Documents.
ACM SIGIR, 1994.
PDF
Barzilay R., Elhadad M.
Using Lexical Chains for Text Segmentation.
EMNLP'97
PDF
Beeferman D., Berger A., Lafferty J.
Text Segmentation Using Exponential Models.
EMNLP'97
Postscript
Beeferman D., Berger A., Lafferty J.
Statistical Models for Text Segmentation.
ML'99
Postscript
Choi F. Y. Y.
Advances in domain independant linear text segmentation.
NAACL'2K
PDF
Hearst M.A., Plaunt C.
Subtopic Structuring for full-length Document Access.
SIGIR 1993
Postscript
Hearst M.A.
Cases as structured indexes for full-length documents.
AAAI 1993
Postscript
Hearst M.A.
TextTiling: A Quantitative Approach to Discourse Segmentation.
Technical Rapport
Postscript
Hearst M.A.
Multi-Paragraph Segmentation of Expository Texts.
ACL 1994
Postscript
Huang X., Peng F., Schuuramns D., Cercone N., Robertson S.E.
Applying Machine Learning to Text Segmentation for Information Retrieval.
Information Retrieval 2003
PDF
Ji X., Zha H.
Domain-independant Text Segmentation using Anisotropic Diffusion and Dynamic Programming.
Sigir 2003
PDF
Kozima H.
Text Segmentation Based on Similarity.
ACL 1993
Postscript
Litman D.J., Passonneau R.J.
Combining Multiple Knowledge Sources for Discourse Segmentation.
ACL 1995
Postscript
Mulbregt, P.van, Carp, I., Gillick, L., Lowe, S., and Yamron, J.
Text Segmentation and Topic Tracking on Broadcast News Via a Hidden Markov Model Approach.
ICSLP 1998
Postscript
Ponte J.M., Croft W.B.
Text Segmentation by Topic.
DL 1997
Postscript
Reyner J. C.
An Automatic Method of Finding Topic Boundaries.
DL 1997
PDF
Salton G., Singhal A., Buckely C., Mitra M.
Automatic Text Decomposition Using Text Segments and Text Themes.
HyperText 1996
Postscript
Cline M.
Utilizing HTML Structure and Linked Pages to Improve Learning for Text Categorization.
Thesis, 1999
PDF
Dumais S., Chen H.
Hierarchical Classification of Web Content.
Sigir 2000
PDF
Kamps J., De Rijke M., Sigurbjörnsson B.
The Importance of Length Normalization for XML Retrieval.
IR Journal 2004
PDF
Yang Y., Slattery S., Ghani R.
A study of Approahces to Hypertext Categorization.
Journal of Intelligent Information Systems 2002
PDF
Banko M., Mittal V., Kantrowitz M., Goldstein J.
Generating Extraction-Based Summaries from Hand-Written Summaries by Aligning Text Spans.
PacLing 1999
Postscript
Barzilay R., Lee L.
Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
HLT-NAACL 2004
PDF
Berger A.L., Mittal V.
Ocelot: A system for summarizing web pages
Research and Development in Information Retrieval
PDF
Chuang W.T., Yang J.
Extracting sentence segments for text summarization: a machine learning approach.
SIGIR 2000
PDF
Farzindar A., Lapalme G.
Legal Text Summarization by Exploration of the Thematic strucutres and Argumentative Roles.
ACL 2004
PDF
Hahn U., Mani I.
The Challenges of Automatic Summarization
IEEE 2000
PDF
Hongyan J., Barzilay R., McKeown K., Elhadad M.
Summarization Evaluation Methods: Experiments and Analysis.
AAAI 1998
Postscript
Hongyan J., McKeown K.
The Decomposition of Human-Written Summary Sentences.
SIGIR 1999
Postscript
Goldstein J.
Automatic Text Summarization of Mutliple Documents.
Technical Report
Postscript
Goldstein J., Kantpowitz M., Mittal V., Carbonell J.,
Summarizing Text Documents: Sentence Selection and Evaluation Metrics.
SIGIR 1999
Postscript
Jing H., Barzilay R., McKeown K., Elhadad M.
Summarization Evaluation Methods: Experiments and Analysis.
AAAI 1999
Postscript
Kruengkrai C., Jaruskulchai C.
Using One-Class SVMs for Relevant Sentence Extraction
International Symposium on Communications and Information Technologies 2003
PDF
Kupiec J., Pederson J., Chen F.
A Trainable Doscument Summarizer.
SIGIR 1995
Postscript
Marcu D.
The Automatic Construction of Large-scale corpora for Summarization Research.
SIGIR 1999
Postscript
Marcu D.
The Rhetorical Parsing of Unrestricted Texts: A Surface-based Approach.
Rapport
PDF
Marcu D.
From discourse structure to text summaries.
ACL/EACL 1997
Postscript
Mitra M., Singhal A., Buckley C.
Automatic Text Summarization by Paragraph Extraction.
ACL 1997
Postscript
Mittal V., Kantrowitz M., Goldstein J., Carbonell J.
Selecting Text Spans for Document Summaries: Heuristics and Metrics.
Technical Rapport
Postscript
Moens M.-F., Angheluta R., Dumortier J.
Generic Technologies for Single- and Multi-document Summarizaton
Information Processing and Management 2005
PDF
Nagao K., Hasida K.
Automatic Text Summarization based on the Global Document Annotation.
COLING 1998
Postscript
Nomoto T., Matsumoto Y.
A new Approach to Unsupervised Text Summarization.
Sigir 2001
PDF
Radev D.R., Jing Hongyan, Budzikowska M.
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies.
NLP 2000
Postscript
Radev D.R., Hovy E., McKeown K.
Introduction on the Special Issue on Summarization.
ACL 2002
PDF
Sakai T., Sparck-Jones K.
Generic summaries for indexing in information retrieval
Sigir 2001
PDF
Summac Report
Summac Tipster Evaluation Program.
1998
Postscript
Taghva K., Gilbreth J.
Recognizing Acronyms and their definitions.
IJDAR 1999 Vol.1
PDF
Teufel S., Moens M.
Sentence Extraction as Classification Task.
Mani and Maybury ed.
Postscript
Teufel S., Moens M.
Sentence extraction and rhetorical classification for flexible abstracts.
AAAI'98
Postscript
Witbrock M.J., Mittal V.O.
Ultra-Summarization: A statistical Approach to Generating Highly Condensed Non-Extractive Summaries
Research and Development in Information Retrieval 1999
Postscript
Zechner K.
Fast Generation of Abstracts from General Domain Text Corpora by Extracting Relevant Sentences.
COLING 1996
Postscript
Zechner K.
Automatic Text Abstracting by Selecting Relevant Passages.
MSc Dissertation
Postscript
Blair-Goldensohn S., McKeown K.R., Hazen A.
A Hybrid Approach for QA Track Definitional Questions
TREC 2003
PDF
Chu-Caroll J., Czuba K., Prager J., Ittcheriah A.
In Question Answering, Two Heads are Better Than One.
NAACL 2003
PDF
Ferret O., Grau B., Illouz G., Jacquemin C., Masson N.
QALC- The Question-Answering program of the Language and Cognition Group at LIMSI-CNRS.
TREC-8 2000
PDF
Ferret O., Grau B., Illouz G., Jacquemin C., Masson N.
QALC- The Question-Answering program of the Language and Cognition Group at LIMSI-CNRS.
TREC-8 2000
PDF
Hovy E., Gerber L., Hermjakob U., Junk M., Lin C.-Y.
Question answering in webclopedia.
TREC 9
PDF
Ravichandran D., Hovy E.
Learning Surface Text Patterns for a Question Answering System.
ACL 2002
PDF
Ramakrishnan G., Chakrabarti S., Paranjpe D., Bhattacharyya P.
Is Question Answering an Acquired Skill?
WWW 2004
PDF
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AAAI 1993
Postscript
Cranor L.F., LaMacchia B.A.
Spam!
ACM 1998
PDF
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Postscript
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Postscript
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ACL, 1991
Postscript
Haines D., Bruce-Croft W.
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SIGIR 1993
Postscript
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A Review of Statistical Language Processing Techniques.
The Queen's University of Belfast, 1995.
Postscript
McMahon J., Smith F.J.
Structural Tags, Annealing and Automatic Word Classification.
Artificial Intelligence and the Simulation of Behaviour Quarterly, 1994.
Postscript
Miller D.R.H., Leek T., Schwartz R.M.
BBN at TREC7: Using Hidden Markov Models for Information Retrieval.
TREC-7, 1999.
PDF
Pereira F., Tibshy N., Lillian L.
Distributional Clustering of English Words.
CL 1993.
Postscript
Salton G., Allan J., Buckley C.
Approaches to Passage Retrieval in Full Text Information Systems.
SIGIR, 1993.
PDF
Tang Y.Y., Cheriet M., Liu J., Said J.N., Suen C.Y
Document Analyis And Recognition By computers
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Postscript
Yarowsky D.
Word sense disambiguation using statistical models of roget's categories trained on large corpora.
Colling 1992
Postscript