M. Kaszkiel, J. Zobel, and R. Sacks-Davis. Efficient passage ranking for document databases. ACM Transactions on Information Systems (TOIS), 17(4):406--439, Oct. 1999. Home/Search Document Details and Download
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HMM-based Passage Models for Document Classification and Ranking - Denoyer, Zaragoza (2001) (1 citation) (Correct)
.... combine the score of document sections for computing the document score and performs tests on the second TREC collection (TREC 2) 4] introduces different types of passages and discusses their role for long documents retrieval, he performed tests on the Tipster collection [7] The recent paper by [10] provides a thorough discussion and evaluation of passage retrieval, they performed tests on the TREC 5 collection . Note that all these works focus on ad hoc retrieval and rely on the adaptation of classical IR document ranking techniques for taking into account text passages instead of whole ....
Kaszkiel M., Zobel J., Davis Sacks-R. Efficient passage ranking for document databases, ACM Trans. on information systems 1999, 17(4):406-439.
Bit-Sliced Index Arithmetic - Rinfret, O'Neil, al. (Correct)
....uses basically the same approach as the most efficient IR algorithm, the Perry Willet Term Matching algorithm, PWTM [PW83] and has the advantage that i t depends only on what we propose as native operations for a DBMS. Some TM algorithms in IR use more complex distance metrics than ours (see [KZS99]) for example by weighting term matches higher for terms that are relatively infrequent. In our concluding section we explain how BSTM can be generalized to more complex metrics. Searching documents for terms in this way is of great interest, of course, as evidenced by Database Vendor products ....
....cited as best in [SALT89] but i n [MURT99] Murtagh cites the Perry Willet al..gorithm [PW83] as an improvement on earlier term matching algorithms used in IR, including his own. The algorithm in [PW83] i s straightforward, and we modify its description slightly to use more modern nomenclature from [MZ96, KZS99]. In the PerryWillet al..gorithm, an index I is understood to exist on the keyword terms of all documents. For each keyword term t in I, there is a sequence of document identifiers: d 1 , d n ) Often these document identifiers have associated weights for the term in the referenced document. In ....
[Article contains additional citation context not shown here]
Marcin Kaszkiel, Justin Zobel, and Ron Sacks-Davis. Efficient Passage Ranking for Document Databases. ACM Transactions on Information Systems, Vol. 17, No. 4, October 1999, Pages 406-439.
ATT at TREC-9 - Amit Singhal Marcin Self-citation (Kaszkiel) (Correct)
....the fact that anchor texts are typically short and positional information, which is mainly needed to enforce proximity, is not that important in this case. The total index size is roughly 15 of the raw web data indexed. At retrieval time, Tivra processes all the inverted lists in document order. [1] The inverted list are stored sorted by the document ids. Tivra reads all the inverted list in one go and runs an efficient merge sort maintaining a heap of top documents. All term weighting is done at this time. Tivra can compute several scores for a document, for example, a score based on ....
M. Kaszkiel, J. Zobel, and R. Sacks-Davis. Efficient passage ranking for document databases. ACM Transaction on Information Systems, 17(4):406-439, October 1999.
ATT at TREC-9 - Amit Singhal Marcin Self-citation (Kaszkiel) (Correct)
....the fact that anchor texts are typically short and positional information, which is mainly needed to enforce proximity, is not that important in this case. The total index size is roughly 15 of the raw web data indexed. At retrieval time, Tivra processes all the inverted lists in document order. [1] The inverted list are stored sorted by the document ids. Tivra reads all the inverted list in one go and runs an efficient merge sort maintaining a heap of top documents. All term weighting is done at this time. Tivra can compute several scores for a document, for example, a score based on ....
M. Kaszkiel, J. Zobel, and R. Sacks-Davis. Efficient passage ranking for document databases. ACM Transaction on Information Systems, 17(4):406--439, October 1999.
Three-Level Caching for Efficient Query Processing in Large Web .. - Long, Suel (2005) (Correct)
No context found.
M. Kaszkiel, J. Zobel, and R. Sacks-Davis. Efficient passage ranking for document databases. ACM Transactions on Information Systems (TOIS), 17(4):406--439, Oct. 1999.
Three-Level Caching for Efficient Query Processing in Large Web .. - Long, Suel (2005) (Correct)
No context found.
M. Kaszkiel, J. Zobel, and R. Sacks-Davis. Efficient passage ranking for document databases. ACM Transactions on Information Systems (TOIS), 17(4):406--439, Oct. 1999.
HMM-based Passage Models for Document Classification and.. - Denoyer, Zaragoza.. (2001) (1 citation) (Correct)
No context found.
Kaszkiel M., Zobel J., Davis Sacks-R. Efficient passage ranking for document databases, ACM Trans. on information systems 1999, 17(4):406-439.
Next-Generation Information Retrieval: Integrating Document and.. - Bremer (2003) (Correct)
No context found.
Marcin Kaszkiel, Justin Zobel, and Ron Sacks-Davis. Efficient Passage Ranking for Document Databases. ACM Transactions on Information Systems, 17(4):406--439, October 1999. 109
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