Lillian Lee
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Publications by Lillian Lee (bibliography)
» 2009 «
Danescu-Niculescu-Mizil, Cristian, Kossinets, Gueorgi, Kleinberg, Jon and Lee, Lillian (2009): How opinions are received by online communities: a case study on amazon.com helpfulness votes. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 141-150. Available online
“There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries.”
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» 2007 «
Lee, Lillian (2007): IDF revisited: a simple new derivation within the Robertson-Spärck Jones probabilistic model. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 751-752. Available online
“There have been a number of prior attempts to theoretically justify the effectiveness of the inverse document frequency (IDF). Those that take as their starting point Robertson and Sparck Jones's probabilistic model are based on strong or complex assumptions. We show that a more intuitively plausible assumption suffices. Moreover, the new assumption, while conceptually very simple, provides a solution to an estimation problem that had been deemed intractable by Robertson and Walker (1997).”
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» 2006 «
Kurland, Oren and Lee, Lillian (2006): Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 83-90. Available online
“We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform reranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via consideration of language models induced from them. We find that our cluster-document graphs give rise to much better retrieval performance than previously proposed document-only graphs do. For example, authority-based reranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs. Moreover, we also show that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.”
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» 2005 «
Kurland, Oren, Lee, Lillian and Domshlak, Carmel (2005): Better than the real thing?: iterative pseudo-query processing using cluster-based language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 19-26. Available online
“We present a novel approach to pseudo-feedback-based ad hoc retrieval that uses language models induced from both documents and clusters. First, we treat the pseudo-feedback documents produced in response to the original query as a set of pseudo-query that themselves can serve as input to the retrieval process. Observing that the documents returned in response to the pseudo-query can then act as pseudo-query for subsequent rounds, we arrive at a formulation of pseudo-query-based retrieval as an iterative process. Experiments show that several concrete instantiations of this idea, when applied in conjunction with techniques designed to heighten precision, yield performance results rivaling those of a number of previously-proposed algorithms, including the standard language-modeling approach. The use of cluster-based language models is a key contributing factor to our algorithms' success.”
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Kurland, Oren and Lee, Lillian (2005): PageRank without hyperlinks: structural re-ranking using links induced by language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 306-313. Available online
“Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.”
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» 2004 «
Kurland, Oren and Lee, Lillian (2004): Corpus structure, language models, and ad hoc information retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 194-201. Available online
“Most previous work on the recently developed language-modeling approach to information retrieval focuses on document-specific characteristics, and therefore does not take into account the structure of the surrounding corpus. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in precision and recall, and our new interpolation algorithm posts statistically significant improvements for both metrics over all three corpora tested.”
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» 2001 «
Ando, Rie Kubota and Lee, Lillian (2001): Iterative residual rescaling. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2001. pp. 154-162. Available online
“We consider the problem of creating document representations in which inter-document similarity measurements correspond to semantic similarity. We first present a novel subspace-based framework for formalizing this task. Using this framework, we derive a new analysis of Latent Semantic Indexing(LSI), showing a precise relationship between its performance and the uniformityof the underlying distribution of documents over topics. This analysis helps explain the improvements gained by Ando's (2000) Iterative Residual Rescaling (IRR) algorithm: IRR can compensate for distributional non-uniformity. A further benefit of our framework is that it provides a well-motivated, effective method for automatically determining the rescaling factor IRR depends on, leading to further improvements. A series of experiments over various settings and with several evaluation metrics validates our claims.”
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Mar 15th, 2010
Changes to this page (author)
16 Feb 2010: Enabled abstracts to be shown on Lillian Lee's author page.09 Jul 2009: Author was edited 12 May 2008: Author was edited
24 Jun 2007: Author was edited
24 Jun 2007: Author was edited
24 Jun 2007: Author was edited
24 Jun 2007: Author was edited
24 Jun 2007: Author was added to the bibliography
