This method is well described in Salton and Voorhees (1985) and in Chapter 15. "Optimization of Inverted Vector Searches." Many combinations of term-weighting can be done using the inner product. Information Storage and Retrieval, 7(5), 217-40. J. Table 14.1 shows some timing results of this pruning algorithm. "The Implementation of a Document Retrieval System," in Research and Development in Information Retrieval, eds. 14.8.3 Ranking and Boolean Systems In 1982, MEDLINE had approximately 600,000 on-line records, with records being added at a rate of approximately 21,000 per month (Doszkocs 1982). SPARCK JONES, K. 1979a. J. SALTON, G. 1971. "Intelligent Information Retrieval Using Rough Set Approximations." It can be very useful to add additional weight for document structure, such as higher weightings for terms appearing in the title or abstract versus those appearing only in the text. K should be set to low values (0.3 was used by Croft) for collections with long (35 or more terms) documents, and to higher values (0.5 or higher) for collections with short documents, reducing the role of within-document frequency. SRINIVASAN, P. 1989. J. "Search Term Relevance Weighting Given Little Relevance Information." If the IDF is greater than or equal to one third the maximum IDF of any term in the data set, then repeat steps 2, 3, and 4. Results are presented in a roughly chronological order to provide some sense of the development of knowledge about ranking through these experiments. IBM J. 4. Documentation, 29(4), 351-72. records retrieved New York: Knowledge Industry Publications, Inc. SPARCK JONES, K. 1972. J. LUCARELLA, D. 1983. Report from the School of Information Studies, Syracuse University, Syracuse, New York. Besides confirming that the best document term-weighting is provided by a product of the within-document term frequency and the IDF, normalized by the cosine measure, they show performance improvements using enhanced query term-weighting measures for queries with term frequencies greater than one. Information Retrieval Experiment. Note that records containing only high-frequency terms will not have any weight added to their accumulator and therefore are not sorted. The top section of Figure 14.1 shows the seven terms in this data set. J. Only those experiments dealing directly with term-weighting and ranking will be discussed here. 3. efficient clustering techniques [Author Willett] "On Relevance, Probabilistic Indexing and Information Retrieval." 1990. SPARCK JONES, K. 1973. terms per query Some time is saved by direct access to memory rather than through hashing, and as many unique postings are involved in most queries, the total time savings may be considerable. J. A major time bottleneck in the basic search process is the sort of the accumulators for large data sets. Not only is this likely to be a faster access method than the binary search, but it also creates an extendable dictionary, with no reordering for updates. MARON, M. E., and J. L. KUHNS. BOOKSTEIN, A. Their inverted file consists of the dictionary containing the terms and pointers to the postings file, but the dictionary is not alphabetically sorted. J. 1976. Average response time 0.28 0.58 1.1 1.6 "Surrogate Subsets: A Free Space Management Strategy for the Index of a Text Retrieval System." This additional weighting needs to be considered with respect to the particular data set being used for searching. This chapter has presented a survey of statistical ranking models and experiments, and detailed the actual implementation of a basic ranking retrieval system. Documentation, 27(4), 254-66. 1983. 1989. "Implementing Ranking Strategies Using Text Signatures." Average number of 797 2843 5869 22654 1983. J. A different approach was taken by Harman (1986). "Construction of Weighted Term Profiles by Measuring Frequency and Specificity in Relevant Items." "The Use of Hierarchic Clustering in Information Retrieval." 14.8 TOPICS RELATED TO RANKING This group included both the cosine correlation and the inner product function used in the probabilistic models. The use of ranking means that strategies needed in Boolean systems to increase precision are not only unnecessary but should be discarded in favor of strategies that increase recall at the expense of precision. J. In this manner the dictionary used in the binary search has only one "line" per unique term. 1977. KNUTH, D. E. 1973. "Optimization of Inverted Vector Searches." WALKER, S., and R. M. JONES. The advantage of this term-weighting option is that updating (assuming only the addition of new records and not modification of old ones) would not require the postings to be changed. Relevance weighting is discussed further in Chapter 11 on relevance feedback. J. Paper presented at the Statistical Association Methods for Mechanized Documentation. The disadvantage of this option is that updating requires changing all postings because the IDF is an integral part of the posting (and the IDF measure changes as any additions are made to the data set). If this is the actual weight stored, then all the calculations of term-weights must be done in the search routine itself, providing a heavy overhead per posting. Information Storage and Retrieval, 7(5), 217-40. 1973. records retrieved Information Storage and Retrieval, 7(5), 217-40. Information Science, 6, 59-66. 14.4.1 Direct Comparison of Similarity Measures and Term-Weighting Schemes M. Williams, pp. "Optimizing Convenient Online Access to Bibliographic Databases." Other collections showed less improvement, but the same relative merit of the term-weighting schemes was found. Average number of 4.1 3.5 3.5 3.5 That study also suggests that the ability of a ranking system to use the smaller inverted files discussed in this chapter makes storage and efficiency of ranking techniques competitive with that of signature files. Because of the predominance of Boolean retrieval systems, several attempts have been made to integrate the ranking model and the Boolean model (for a summary, see Bookstein ). Information Science, 15, 249-60. The combination of the within-document frequency with the IDF weight often provides even more improvement. 1989), document and query structures are also used to influence the ranking, increasing term-weights for terms in titles of documents and decreasing term weights for terms added to a query from a thesaurus. The SMART Retrieval System -- Experiments in Automatic Document Processing. SALTON, G., and M. MCGILL. 1973. SRINIVASAN, P. 1989. This tailoring seems to be particularly critical for manually indexed or controlled vocabulary data where use of within-document frequencies may even hurt performance. This chapter has presented a survey of statistical ranking models and experiments, and detailed the actual implementation of a basic ranking retrieval system. More details of the storage and use of these files is given in the description of the search process. ), Annual Review of Information Science and Technology, ed. Association for Computing Machinery, 24(3), 418-27. 1990. If a query has only high-frequency terms (several user queries had this problem), then pruning cannot be done (or a fancier algorithm needs to be created). SPARCK JONES, K. 1981. The other pruning techniques mentioned earlier should result in the same magnitude of time savings, making pruning techniques an important issue for ranking retrieval systems needing fast response times. 1980. Various methods have been developed for dealing with this problem. 109-45. J. Doszkocs solved the problem in his experimental front-end to MEDLINE (the CITE system) by segmenting the inverted file into 8K segments, each holding about 48,000 records, and then hashing these record addresses into the fixed block of accumulators. where This chapter has presented a survey of statistical ranking models and experiments, and detailed the actual implementation of a basic ranking retrieval system. First, it is very important to normalize the within-document frequency in some manner, both to moderate the effect of high-frequency terms in a document (i.e., a term appearing 20 times is not 20 times as important as one appearing only once) and to compensate for document length. "A Statistical Interpretation of Term Specificity and Its Application in Retrieval." "Operations Research Applied to Document Indexing and Retrieval Decisions." records retrieved 1977. M. Williams, pp. Introduction to Modern Information Retrieval. Whereas the cosine similarity is used here with raw frequency term-weighting only (at least in the experiment described in Noreault, Koll and McGill ), any of the term-weighting functions described in section 14.5 could be used. Although the hash access method is likely faster than a binary search, the processing of the linked postings records and the search-time term-weighting will hurt response time considerably. Documentation, 35(1), 30-48. The user may request ranked output. J. American Society for Information Science, 26(5), 280-89. N = the number of documents in the collection BERNSTEIN, L. M., and R. E. WILLIAMSON. Harman and Candela (1990) found that almost every user query had at least one term that had postings in half the data set, and usually at least three quarters of the data set was involved in most queries. SPARCK JONES, K. 1979b. 4. BELKIN, N. J. and W. B. CROFT. Full-text indexing was used on various standard test collections, with full-text indexing also done on the queries. 14.5 A GUIDE TO SELECTING RANKING TECHNIQUES maxnoise = the highest noise of any term in the collection The use of ranking means that there is little need for the adjacency operations or field restrictions necessary in Boolean. Average number of 797 2843 5869 22654 Doctoral dissertation, Jesus College, Cambridge, England. Note that this combining of sets for complex Boolean queries can be a complicated operation. "Optimizing Convenient Online Access to Bibliographic Databases." J. BUCKLEY, C., and A. LEWIT. "Retrieval Techniques," in Williams, M. There was a lack of significant difference between pairs of term-weighting measures for uncontrolled vocabulary, however, which could indicate that the difference between linear combinations of term-weighting schemes is significant but that individual pairs of term-weighting schemes are not significantly different. In the area of parsing, this may mean relaxing the rules about hyphenation to create indexing both in hyphenated and nonhyphenated form. BOOKSTEIN, A. Documentation, 35(1), 30-48. 1989. Documentation, 32(4), 294-317. 1960. It is possible to provide ranking using signature files (for details on signature files, see Chapter 4 on that subject). Information Storage and Retrieval, 9(11), 619-33. Research and Development, 1(4), 309-17. Possibly the use of two separate dictionaries, both mapping to the same hybrid posting file, would improve search time without the loss of storage efficiency, but this has not been tried. SALTON, G., and C. S. YANG. N = the number of documents in the collection Berlin: Springer-Verlag. Perry and Willett (1983) and Lucarella (1983) also described methods of reducing the number of cells involved in this final sort. Paper presented at the Sixth International Conference on Research and Development in Information Retrieval, Bethesda, Maryland. 1979. In the area of parsing, this may mean relaxing the rules about hyphenation to create indexing both in hyphenated and nonhyphenated form. 1983. 1985. 1987. 1984. This requires a sequential storage of the postings in the index, with the postings pointer in the dictionary being used to control the location of the read operation, and the number of postings (also stored in the dictionary) being used to control the length of the read (and the separation of the butfer). If option 3 was used for weighting, then this total is immediately available and only a simple addition is needed. Information Storage and Retrieval, 9(11), 619-33. "SIBRIS: the Sandwich Interactive Browsing and Ranking Information System." Croft (1983) expanded his combination weighting scheme to incorporate within-document frequency weights, again using a tuning factor K on these weights to allow tailoring to particular collections. Paper presented at the Third Joint BCS and ACM symposium on Research and Development in Information Retrieval, Cambridge, England. (Ed. Documentation, 35(1), 30-48. C should be set to low values (near 0) for automatically indexed collections, and to higher values such as 1 for manually indexed collections. ), Annual Review of Information Science and Technology, ed. 2. Sort the accumulators with nonzero weights to produce the final ranked record list. Association for Computing Machinery, 7(3), 216-44. M. Williams, pp. First, the I/O needs to be minimized. "The Use of Hierarchic Clustering in Information Retrieval." K should be set to low values (0.3 was used by Croft) for collections with long (35 or more terms) documents, and to higher values (0.5 or higher) for collections with short documents, reducing the role of within-document frequency. In SIBRIS, an operational information retrieval system (Wade et al. J. American Society for Information Science, 35(4), 235-47. A check needs to be made after step 1 for this. HARPER, D. J. PERRY, S. A., and P. WILLETT. 14.8.4 Use of Ranking in Two-level Search Schemes SALTON, G., H. WU, and C. T. YU. J. clustering using "nearest neighbor" techniques Association for Computing Machinery, 24(3), 418-27. Some time is saved by direct access to memory rather than through hashing, and as many unique postings are involved in most queries, the total time savings may be considerable. SPARCK JONES, K. 1979a. 1989. She found that when using the single measures alone, the distribution of the term within the collection improved performance almost twice as much for the Cranfield collection as using only within-document frequency. 14.4.3 Ranking Techniques Used in Operational Systems Report from the School of Information Studies, Syracuse University, Syracuse, New York. 1988. "A Review of the Use of Inverted Files for Best Match Searching in Information Retrieval Systems." These situations can be accommodated by the basic ranking search system using a two-level search. The system accepts queries that are either Boolean logic strings (similar to many commercial on-line systems) or natural language queries (processed as Boolean queries with implicit OR connectors between all query terms). YU, C. T., and G. SALTON. 14.5 A GUIDE TO SELECTING RANKING TECHNIQUES This chapter describes the implementation of a ranking system and is organized in the following manner. HARMAN, D. 1986. Although the hash access method is likely faster than a binary search, the processing of the linked postings records and the search-time term-weighting will hurt response time considerably. Buckley and Lewit (1985) presented an elaborate "stopping condition" for reducing the number of accumulators to be sorted without significantly affecting performance. The algorithm currently ranks the posts each user sees in the order that they’re likely to enjoy them, based on a variety of factors, a.k.a ranking signals. : Addison-Wesley. In the area of parsing, this may mean relaxing the rules about hyphenation to create indexing both in hyphenated and nonhyphenated form. It was also suggested that clustering could improve the performance of retrieval by pregrouping like documents (Jardine and van Rijsbergen 1971). This method was used in the prototype built by Harman and Candela (1990) and provided a very effective way of handling phrases and other limitations without increasing indexing overhead. As can be seen, the response times are greatly affected by pruning. Ranking retrieval systems and relevance feedback have been closely connected throughout the past 25 years of research. Information Storage and Retrieval, 7(5), 217-40. But the major takeaways from this article should be to understand the why and what of decision makers. Read the entire postings file for that term into a buffer and add the term weights for each record id into the contents of the unique accumulator for the record id. The first intuitive answer may be to simply order them according to their win-loss records. The penalty paid for this efficiency is the need to update the index as the data set changes. where New York: Elsevier Science Publishers. This would require a different organization of the final inverted index file that contains the dictionary, but would not affect the postings lists (which would be sequentially stored for search time improvements). per query (no pruning) 14.9 SUMMARY 28-37. "Computer Evaluation of Indexing and Text Processing." Documentation, 27(4), 254-66. For further details on clustering and its use in ranking systems, see Chapter 16. Section 14.6 describes the implementation of a basic ranking retrieval system, with section 14.7 showing possible variations to this scheme based on retrieval environments. 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