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  • × author_ss:"Lee, J.-H."
  • × year_i:[2000 TO 2010}
  1. Na, S.-H.; Kang, I.-S.; Lee, J.-H.: Adaptive document clustering based on query-based similarity (2007) 0.00 
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    Abstract
    In information retrieval, cluster-based retrieval is a well-known attempt in resolving the problem of term mismatch. Clustering requires similarity information between the documents, which is difficult to calculate at a feasible time. The adaptive document clustering scheme has been investigated by researchers to resolve this problem. However, its theoretical viewpoint has not been fully discovered. In this regard, we provide a conceptual viewpoint of the adaptive document clustering based on query-based similarities, by regarding the user's query as a concept. As a result, adaptive document clustering scheme can be viewed as an approximation of this similarity. Based on this idea, we derive three new query-based similarity measures in language modeling framework, and evaluate them in the context of cluster-based retrieval, comparing with K-means clustering and full document expansion. Evaluation result shows that retrievals based on query-based similarities significantly improve the baseline, while being comparable to other methods. This implies that the newly developed query-based similarities become feasible criterions for adaptive document clustering.
    Type
    a
  2. Na, S.-H.; Kang, I.-S.; Lee, J.-H.: Parsimonious translation models for information retrieval (2007) 0.00 
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    Abstract
    In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user's information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.
    Type
    a
  3. Kang, I.-S.; Na, S.-H.; Lee, S.; Jung, H.; Kim, P.; Sung, W.-K.; Lee, J.-H.: On co-authorship for author disambiguation (2009) 0.00 
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    Abstract
    Author name disambiguation deals with clustering the same-name authors into different individuals. To attack the problem, many studies have employed a variety of disambiguation features such as coauthors, titles of papers/publications, topics of articles, emails/affiliations, etc. Among these, co-authorship is the most easily accessible and influential, since inter-person acquaintances represented by co-authorship could discriminate the identities of authors more clearly than other features. This study attempts to explore the net effects of co-authorship on author clustering in bibliographic data. First, to handle the shortage of explicit coauthors listed in known citations, a web-assisted technique of acquiring implicit coauthors of the target author to be disambiguated is proposed. Then, a coauthor disambiguation hypothesis that the identity of an author can be determined by his/her coauthors is examined and confirmed through a variety of author disambiguation experiments.
    Type
    a
  4. Lee, J.-H.; Park, S.; Ahn, C.-M.; Kim, D.: Automatic generic document summarization based on non-negative matrix factorization (2009) 0.00 
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    Abstract
    In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence selection. However, the obtained results are less meaningful, because singular vectors are used as the bases for sentence selection from given documents, and singular vector components can have negative values. We propose a new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization. The proposed method uses non-negative constraints, which are more similar to the human cognition process. As a result, the method selects more meaningful sentences for generic document summarization than those selected using LSA.
    Type
    a
  5. Kang, I.-S.; Na, S.-H.; Kim, J.; Lee, J.-H.: Cluster-based patent retrieval (2007) 0.00 
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    Abstract
    Through the recent NTCIR workshops, patent retrieval casts many challenging issues to information retrieval community. Unlike newspaper articles, patent documents are very long and well structured. These characteristics raise the necessity to reassess existing retrieval techniques that have been mainly developed for structure-less and short documents such as newspapers. This study investigates cluster-based retrieval in the context of invalidity search task of patent retrieval. Cluster-based retrieval assumes that clusters would provide additional evidence to match user's information need. Thus far, cluster-based retrieval approaches have relied on automatically-created clusters. Fortunately, all patents have manually-assigned cluster information, international patent classification codes. International patent classification is a standard taxonomy for classifying patents, and has currently about 69,000 nodes which are organized into a five-level hierarchical system. Thus, patent documents could provide the best test bed to develop and evaluate cluster-based retrieval techniques. Experiments using the NTCIR-4 patent collection showed that the cluster-based language model could be helpful to improving the cluster-less baseline language model.
    Type
    a
  6. Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007) 0.00 
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    Abstract
    The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC.
    Type
    a

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