Lillian Lee

Publications

General audience

• "I'm sorry Dave, I'm afraid I can't do that": Linguistics, statistics, and natural language processing circa 2001.
  Lillian Lee.
  Computer Science: Reflections on the Field, Reflections from the Field, National Academies Press, pp. 111–118, 2004.
• A matter of opinion: Sentiment analysis and business intelligence (position paper).
  Lillian Lee.
  Written for the IBM Faculty Summit on the Architecture of On-Demand Business, 2004.

Sentiment analysis

• Opinion mining and sentiment analysis.
  Bo Pang and Lillian Lee.
  Foundations and Trends in Information Retrieval 2(1-2), pp. 1&ndash135, July 2008.
  A monograph surveying the field (note the singular).
• The power of negative thinking: Exploiting label disagreement in the min-cut classification framework.
  Mohit Bansal, Claire Cardie, and Lillian Lee.
  Proceedings of COLING, 2008. Poster paper.
• Using very simple statistics for review search: An exploration.
  Bo Pang and Lillian Lee.
  Proceedings of COLING, 2008. Poster paper.
• Get out the vote: Determining support or opposition from Congressional floor-debate transcripts.
  Matt Thomas, Bo Pang, and Lillian Lee.
  Proceedings of EMNLP, pp. 327–335, 2006.
• Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.
  Bo Pang and Lillian Lee.
  Proceedings of the ACL, pp. 115–124, 2005.
• A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts.
  Bo Pang and Lillian Lee.
  Proceedings of the 42nd ACL, pp. 271–278, 2004.
• Thumbs up? Sentiment classification using machine learning techniques.
  Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan.
  Proceedings of EMNLP 2002, pp. 79–86.

Information retrieval

• IDF revisited: A simple new derivation within the Robertson-Spärck Jones probabilistic model.
  Lillian Lee.
  Procedings of SIGIR, pp. 751–752, 2007. Poster paper.
• Respect my authority! HITS without hyperlinks, utilizing cluster-based language models.
  Oren Kurland and Lillian Lee.
  Proceedings of SIGIR, pp. 83–90, 2006.
• PageRank without hyperlinks: Structural re-ranking using links induced by language models.
  Oren Kurland and Lillian Lee.
  Proceedings of SIGIR, pp. 306–313, 2005.
• Better than the real thing? Iterative pseudo-query processing using cluster-based language models.
  Oren Kurland, Lillian Lee, and Carmel Domshlak.
  Proceedings of SIGIR, pp. 19–26, 2005.
• Corpus structure, language models, and ad hoc information retrieval.
  Oren Kurland and Lillian Lee.
  Proceedings of SIGIR, pp. 194–201, 2004.
• Iterative residual rescaling: An analysis and generalization of LSI.
  Rie Kubota Ando and Lillian Lee.
  Proceedings of the 24th SIGIR, pp. 154–162, 2001.

Generation

• Catching the drift: Probabilistic content models, with applications to generation and summarization.
  Regina Barzilay and Lillian Lee.
  Proceedings of HLT-NAACL, pp. 113–120, 2004.
• Learning to paraphrase: An unsupervised approach using multiple-sequence alignment.
  Regina Barzilay and Lillian Lee.
  Proceedings of HLT/NAACL 2003, pp. 16–23.
• Bootstrapping lexical choice via multiple-sequence alignment.
  Regina Barzilay and Lillian Lee.
  Proceedings of EMNLP, pp. 164–171, 2002.

Distributional similarity

• On the effectiveness of the skew divergence for statistical language analysis.
  Lillian Lee.
  Artificial Intelligence and Statistics 2001, pp 65–72, 2001.
• Measures of Distributional Similarity.
  Lillian Lee.
  Proceedings of the 37th ACL, pp 25–32, 1999.
• Distributional similarity models: Clustering vs. nearest neighbors.
  Lillian Lee and Fernando Pereira.
  Proceedings of the 37th ACL, pp 33–40, 1999.
• Similarity-based models of word cooccurrence probabilities (pre-publication version).
  Ido Dagan, Lillian Lee, and Fernando Pereira. 
  Machine Learning 34(1-3), special issue on natural language learning, pp 43–69, 1999.
• Similarity-Based Methods for Word Sense Disambiguation.
  Ido Dagan, Lillian Lee, and Fernando Pereira.
  Proceedings of the 35th ACL/8th EACL, pp 56–63, 1997.
• Similarity-Based Approaches to Natural Language Processing.
  Lillian Lee.
  Ph.D. thesis.
  Harvard University Technical Report TR-11-97.
• Similarity-Based Estimation of Word Cooccurrence Probabilities.
  Ido Dagan, Fernando Pereira, and Lillian Lee.
  Proceedings of the 32nd ACL, pp 272–278, 1994.
• Distributional clustering of English words.
  Fernando Pereira, Naftali Tishby, and Lillian Lee.
  Proceedings of the 31st ACL, pp 183–190, 1993.

Of related interest: Baker and McCallum's SIGIR '98 paper, Distributional Clustering of Words for Text Classification, favorably compares [PTL 93] to LSI and other algorithms.  Segmentation

• Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences (pre-publication version).
  Rie Kubota Ando and Lillian Lee.
  Natural Language Engineering 9(2), pp. 127–149, 2003.
• Mostly-Unsupervised Statistical Segmentation of Japanese: Applications to Kanji.
  Rie Kubota Ando and Lillian Lee.
  First Conference of the NAACL, pp. 241–248, 2000.
• Unsupervised Statistical Segmentation of Japanese Kanji Strings.
  Rie Ando and Lillian Lee.
  Cornell University CS Technical Report TR99-1756, 1999.

Context-free languages

• Fast Context-Free Grammar Parsing Requires Fast Boolean Matrix Multiplication.
  Lillian Lee.
  Journal of the ACM 49(1), pp. 1-15, 2002.
  Conference version appeared in the Proceedings of the 35th ACL/8th EACL, pp 9–15, 1997.
• Learning of Context-Free Languages: A Survey of the Literature.
  Lillian Lee.
  Harvard University Technical Report TR-12-96 (written in 1994).

Reviews and pedagogy

• A new start: Innovative introductory AI-centered courses at Cornell.
  Eric Breck, David Easley, K-Y Daisy Fan, Jon Kleinberg, Lillian Lee, Jennifer Wofford, and Ramin Zabih.
  AAAI Spring Symposium on Using AI to Motivate Greater Participation in Computer Science, AAAI Technical Report SS-08-08, pp. 8-13, 2008.
• A non-programming introduction to computer science via NLP, IR, and AI.
  Lillian Lee.
  ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp 32–37, 2002.
• Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze [review] (prepublication version).
  Lillian Lee.
  Computational Linguistics 26(2), pp 277–279, 2000.