Kamal Nigam

Contact Information

(412) 999-5918

knigam at kamalnigam dot com

Areas of Expertise

I have experience in scientific and engineering leadership, machine learning, data mining, information retrieval, information extraction, and text categorization. For more details, please consult my resume (though I'm not on the job market).

Teaching

15-505: Internet Search Technologies, Fall 2007. Co-teaching with Alona Fyshe, Scott Larsen and Chris Monson.

Publications

Sentiment and Polarity Analysis:

Kamal Nigam and Matthew Hurst. Towards a Robust Metric of Polarity. In Shanahan, J., Qu, J. & Wiebe, J. (Eds.). Computing Attitude and Affect in Text: Theory and Applications. Springer, Dordrecht, The Netherlands. 2006.
Kamal Nigam and Matthew Hurst. Towards a Robust Metric of Opinion. In AAAI Spring Symposium on Exploring Attitude and Affect in Text. 2004. (Also see the slightly expanded version appearing as a book chapter.) [PDF]
Matthew Hurst and Kamal Nigam. Retrieving Topical Sentiments from Online Document Collections. In Document Recognition and Retrieval XI. pp. 27--34. 2004. [PDF]

Systems for Extracting and Analyzing Internet Data:

Natalie Glance, Matthew Hurst, Kamal Nigam, Matthew Siegler, Robert Stockton and Takashi Tomokiyo. Deriving Marketing Intelligence from Online Discussion. Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005). 2005. [PDF]
Natalie Glance, Matthew Hurst, Kamal Nigam, Matthew Siegler, Robert Stockton and Takashi Tomokiyo. Analyzing Online Discussion for Marketing Intelligence. 14th International World Wide Web Conference. 2005. (Also see the longer version at KDD) [PDF]
Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. Automating the Construction of Internet Portals with Machine Learning. Information Retrieval. 3(2). pp. 127-163. 2000. [GZipped Postscript] [PDF]
Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, Sean Slattery. Learning to Construct Knowledge Bases from the World Wide Web. Artificial Intelligence, 118(1-2). pp 69-114. 2000. [GZipped Postscript] [PDF]
Rayid Ghani, Rosie Jones, Dunja Mladenic, Kamal Nigam and Sean Slattery. Data Mining on Symbolic Knowledge Extracted from the Web. In KDD-2000 Workshop on Text Mining. 2000. [Postscript] [PDF]
Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. A Machine Learning Approach to Building Domain-Specific Search Engines. In The Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99). 1999. (Also see a longer version in the Information Retrieval Journal) [Postscript] [PDF]
Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. Building Domain-Specific Search Engines with Machine Learning Techniques. In AAAI Spring Symposium on Intelligent Agents in Cyberspace. 1999. (Also see a longer version in the Information Retrieval Journal) [Postscript] [PDF]
Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, Sean Slattery. Learning to Extract Knowledge from the World Wide Web. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 509-516. 1998. (Also see a longer journal paper) [Postscript]
Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, Sean Slattery. Learning to Extract Knowledge from the World Wide Web. Technical Report CMU-CS-98-122. Carnegie Mellon University. 1998. (This is superseded by a newer version appearing in the Artificial Intelligence Journal) [GZipped Postscript]

Text Learning with Unlabeled Data:

Kamal Nigam, Andrew McCallum and Tom Mitchell. Semi-supervised Text Classification Using EM. In Chapelle, O., Zien, A., and Scholkopf, B. (Eds.) Semi-Supervised Learning. MIT Press: Boston. 2006.
Kamal Nigam. Using Unlabeled Data to Improve Text Classification. Doctoral Dissertation, Computer Science Department, Carnegie Mellon University. Technical Report CMU-CS-01-126. 2001. [Postscript] [GZipped Postscript] [PDF]
Kamal Nigam and Rayid Ghani. Analyzing the Effectiveness and Applicability of Co-training. In Ninth International Conference on Information and Knowledge Management (CIKM-2000), pp. 86-93. 2000. [Postscript] [PDF]
Andrew McCallum, Kamal Nigam and Lyle Ungar. Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching. In Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000). 2000. [Postscript] [PDF]
Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2/3). pp. 103-134. 2000. [Postscript] [PDF]
Kamal Nigam and Rayid Ghani. Understanding the Behavior of Co-training. In KDD-2000 Workshop on Text Mining. 2000. (Also see a longer version appearing in CIKM-2000.) [Postscript]
Andrew McCallum and Kamal Nigam. Text Classification by Bootstrapping with Keywords, EM and Shrinkage. In ACL '99 Workshop for Unsupervised Learning in Natural Language Processing, pp. 52-58. 1999. [Postscript] [PDF]
Rosie Jones, Andrew McCallum, Kamal Nigam and Ellen Riloff. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, pp. 52-63. 1999. [Postscript] [PDF]
Andrew McCallum and Kamal Nigam. Employing EM and Pool-Based Active Learning for Text Classification. In Machine Learning: Proceedings of the Fifteenth International Conference (ICML '98), pp. 359-367. 1998. [Postscript] [PDF]
Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Learning to Classify Text from Labeled and Unlabeled Documents. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 792-799. 1998. (Also see a longer version appearing in the Machine Learning Journal) [Postscript] [PDF]
Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Using EM to Classify Text from Labeled and Unlabeled Documents. Technical Report CMU-CS-98-120. Carnegie Mellon University. 1998. (This is superseded by a newer version appearing in the Machine Learning Journal) [Postscript] [PDF]
Kamal Nigam and Andrew McCallum. Pool-Based Active Learning for Text Classification. In Workshop on Learning from Text and the Web, Conference on Automated Learning and Discovery (CONALD). 1998. (This is superseded by a newer version that appeared in ICML-98) [Postscript] [PDF]

General Text Learning:

Kamal Nigam, John Lafferty and Andrew McCallum. Using Maximum Entropy for Text Classification. In IJCAI-99 Workshop on Machine Learning for Information Filtering, pp. 61-67. 1999. [Postscript] [PDF]
Mark Craven, Sean Slattery, and Kamal Nigam. First-Order Learning for Web Mining. In Proceedings of the 10th European Conference on Machine Learning (ECML-98), pp. 250-255. 1998. [Postscript] [PDF]
Andrew McCallum and Kamal Nigam. A Comparison of Event Models for Naive Bayes Text Classification. In AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. Technical Report WS-98-05. AAAI Press. 1998. [Postscript] [PDF]