This method is based on the fact that most records for queries are retrieved based on matching only query terms of high data set frequency. Paper presented at the Second International Cranfield Conference on Mechanized Information Storage and Retrieval Systems, Cranfield, Bedford, England. User weighting can also be considered as additional weighting, although this type of weighting has generally proven unsatisfactory in the past. 14.3.4 Set-Oriented Ranking Models New York: McGraw-Hill. LOCHBAUM, K. E., and L. A. STREETER. Association for Computing Machinery, 25(1), 67-80. The top section of Figure 14.1 shows the seven terms in this data set. An Evaluation of Factors Affecting Document Ranking by Information Retrieval Systems. "An Experimental Study of Factors Important in Document Ranking." Association for Computing Machinery, 25(1), 67-80. wij = freqij X IDFi As can be expected, the search process needs major modifications to handle these hybrid inverted files. 14.7.1 Handling Both Stemmed and Unstemmed Query Terms Doctoral dissertation, Jesus College, Cambridge, England. J. "Intelligent Information Retrieval Using Rough Set Approximations." After stemming, each term in the query is checked against the inverted file (this could be done by using the binary search described in section 14.6). 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). The use of relevance weighting after some initial retrieval is very effective. For more details see Doszkocs (1982). 1984. Table 14.1:: Response Time J. American Society for Information Science, 35(4), 235-47. "Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, 24(5), 513-23. "Operations Research Applied to Document Indexing and Retrieval Decisions." The index shown is a straightforward inverted file, created once per major update (thus only once for a static data set), and is used to provide the necessary speed for searching. J. Size of Data Set 1.6 Meg 50 Meg 268 Meg 806 Meg Because users are often most concerned with recent records, they seldom request to search many segments. J. American Society for Information Science, 26(5), 280-89. For performing predictive analysis in the VoD framework, one needs to process the massive CDR data as well as the deep packet inspection (DPI) data to gather information on videos watched and URLs accessed. HARTER, S. P. 1975. HARPER, D. J. The inverted file presented here will assume that only record location is necessary. Freqik = the frequency of term i in document k The basic search process is therefore unchanged except that instead of each record of the data set having a unique accumulator, the accumulators hold only a subset of the records and each subset is processed as if it were the entire data set, with each set of results shown to the user. These records are still sorted, but serve only to increase sort time, as they are seldom, if ever, useful. 5. 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. This storage savings is at the expense of some additional search time and therefore may not be the optimal solution. "On the Specification of Term Values in Automatic Indexing." (National Bureau of Standards Miscellaneous Publication 269). 14.9 SUMMARY CUTTING, D., and J. PEDERSEN. 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. Table 14.1 shows some timing results of this pruning algorithm. J. American Society for Information Science, 35(4), 235-47. J. Paper presented at the Statistical Association Methods for Mechanized Documentation. 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. Relevance Feedback in Document Retrieval Systems: An Evaluation of Probabilistic Strategies. Whereas the storage for the "accumulators" can be hashed to avoid having to hold one storage area for each data set record, this is definitely not necessary for smaller data sets, and may not be useful except for extremely large data sets such as those used in CITE (which need even more modification; see section 14.7.2). 1989. "The Construction of a Thesaurus Automatically from a Sample of Text." where There are several reasons why this improvement is inconsistent across collections. This method is well described in Salton and Voorhees (1985) and in Chapter 15. Whereas the cosine similarity is used here with raw frequency term-weighting only (at least in the experiment described in Noreault, Koll and McGill [1977]), any of the term-weighting functions described in section 14.5 could be used. The query is parsed using the same parser that was used for the index creation, with each term then checked against the stoplist for removal of common terms. Information Science, 6, 59-66. "Precision Weighting -- An Effective Automatic Indexing Method." 14.8.1 Ranking and Relevance Feedback This storage savings is at the expense of some additional search time and therefore may not be the optimal solution. Although other small-scale operational systems using ranking exist, often their ranking algorithms are not clear from publications, and so these are not listed here. The basic indexing and search processes described in section 14.6 suggest no manner of coping with this problem, as the original record terms are not stored in the inverted file; only their stems are used. 1973. 14.8.3 Ranking and Boolean Systems LUCARELLA, D. 1983. Store a normalized frequency. DOSZKOCS, T. E. 1982. SALTON, G., and M. E. LESK. 1989. 3. Average response time 0.38 1.2 2.6 4.1 The search time for this method is heavily dependent on the number of retrieved records and becomes prohibitive when used on large data sets. G. Salton and H. J. Schneider, pp. J. It is assumed that a natural language query is passed to the search process in some manner, and that the list of ranked record id numbers that is returned by the search process is used as input to some routine which maps these ids onto data locations and displays a list of titles or short data descriptors for user selection. "Index Term Weighting." Others have tried more complex term distributions, most notably the 2-Poisson model proposed by Bookstein and Swanson (1974) and implemented and tested by Harter (1975) and Raghavan et al. A final time savings on I/O could be done by loading the dictionary into memory when opening a data set. SALTON, G., H. WU, and C. T. YU. 1985. Signature files have also been used in SIBRIS, an operational information retrieval system (Wade et al. Paper presented at the Second International Cranfield Conference on Mechanized Information Storage and Retrieval Systems, Cranfield, Bedford, England. CROFT, W. B., and P. SAVINO. Berlin: Springer-Verlag. In looking at results from all the experiments, some trends clearly emerge. The index shown is a straightforward inverted file, created once per major update (thus only once for a static data set), and is used to provide the necessary speed for searching. records retrieved The level of detail is somewhat less than in section 14.6, either because less detail is available or because the implementation of the technique is complex and details are left out in the interest of space. The level of detail is somewhat less than in section 14.6, either because less detail is available or because the implementation of the technique is complex and details are left out in the interest of space. The OPAKI project (Walker and Jones 1987) worked with on-line catalogs and also used the IDF measure alone. 1971. Extensions to this basic system have been shown that modify the basic system to efficiently handle different retrieval environments. 1987. DENNIS, S. F. 1964. The use of term-weighting based on the distribution of a term within a collection always improves performance (or at minimum does not hurt performance).The IDF measure has been commonly used, either in its form as originally used, or in a form somewhat normalized. RAGHAVAN, V. V., H. P. SHI, and C. T. YU. 1980. "Using Probabilistic Models of Document Retrieval Without Relevance Information." The basic indexing and search processes described in section 14.6 suggest no manner of coping with this problem, as the original record terms are not stored in the inverted file; only their stems are used. The implementation will be described as two interlocking pieces: the indexing of the text and the using (searching) of that index to return a ranked list of record identification numbers (ids). SPARCK JONES, K. 1979a. Association for Computing Machinery, 7(3), 216-44. IBM J. "A Review of the Use of Inverted Files for Best Match Searching in Information Retrieval Systems." Perry and Willett (1983) and Lucarella (1983) also described methods of reducing the number of cells involved in this final sort. This hybrid dictionary is in alphabetic stem order, with the terms sorted within the stem, and contains the stem, the number of postings and IDF of the stem, the term, the number of postings and IDF of the term, a bit to indicate if the term is stemmed or not stemmed, and the offset of the postings for this stem/term combination. Either of the following normalized within-document frequency measures can be safely used. This tailoring seems to be particularly critical for manually indexed or controlled vocabulary data where use of within-document frequencies may even hurt performance. lengthj = the number of unique terms in document j The most well known of the set-oriented models are the clustering models where a query is ranked against a hierarchically grouped set of related documents. Information Technology: Research and Development, 2(1), 1-21. These records can be retrieved in the normal manner, but pruned before addition to the retrieved record list (and therefore not sorted). This storage savings is at the expense of some additional search time and therefore may not be the optimal solution. "Using Probabilistic Models of Document Retrieval Without Relevance Information." HARTER, S. P. 1975. MCGILL, M., M. KOLL, and T. NOREAULT. Several operational retrieval systems have implemented ranking algorithms as central to their search mechanism. There are four major options for storing weights in the postings file, each having advantages and disadvantages. Documentation, 32(4), 294-317. They did experiments using all the relevance judgments to weight the terms to see what the optimal performance would be, and also used relevance judgments from half the collection to weight the terms for retrieval from the second half of the collection. SPARCK JONES, K. 1973. IBM J. 1976. 1979. Average number of 797 2843 5869 22654 Sort all query terms (stems) by decreasing IDF value. "Probability and Fuzzy-Set Applications to Information Retrieval," in Annual Review of Information Science and Technology, ed. CROFT, W. B., and L. RUGGLES. The SMART Retrieval System -- Experiments in Automatic Document Processing. "Intelligent Information Retrieval Using Rough Set Approximations." 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). Their inverted file consists of the dictionary containing the terms and pointers to the postings file, but the dictionary is not alphabetically sorted. freqij = the frequency of term i in document j The noise measure consistently slightly outperformed the IDF (however with no significant difference). Croft and Savino (1988) provide a ranking technique that combines the IDF measure with an estimated normalized within-document frequency, using simple modifications of the standard signature file technique (see the chapter on signature files). MCGILL, M., M. KOLL, and T. NOREAULT. 1984. This necessity for ease of update also changes the postings structure, which becomes a series of linked variable length lists capable of infinite update expansion. "A Probabilistic Approach to Automatic Keyword Indexing." Models based on fuzzy set theory have been proposed (for a summary, see Bookstein [1985]) but have not received enough experimental implementations to be used in practice (except when combined with Boolean queries such as in the P-Norm discussed in Chapter 15). An enhancement of this stemming option would be to allow the user to specify a "don't stem" character, and the modifications necessary to handle this are given in section 14.7.1. M. Williams, pp. 1979. G. Salton and H. J. Schneider, pp. An Evaluation of Factors Affecting Document Ranking by Information Retrieval Systems. CROFT, W. B. The term-weighting is done in the search process using the raw frequencies stored in the postings lists. The basic ranking search methodology described in the chapter is so fast that it is effective to use in situations requiring simple restrictions on natural language queries. Several other models have been used in developing term-weighting measures. Average response time 0.28 0.58 1.1 1.6 This method eliminates the often-wrong Boolean syntax used by end-users, and provides some results even if a query term is incorrect, that is, it is not the term used in the data, it is misspelled, and so on. This extension, however, limits the Boolean capability and increases response time when using Boolean operators. Do a binary search for the first term (i.e., the highest IDF) and get the address of the postings list for that term. Berlin: Springer-Verlag. 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. "Optimization of Inverted Vector Searches." Paper presented at ACM Conference on Research and Development in Information Retrieval, Brussels, Belgium. SPARCK JONES, K. 1972. Perry and Willett (1983) and Lucarella (1983) also described methods of reducing the number of cells involved in this final sort. J. American Society for Information Science, 27(3), 129-46. 4. 1990. 14.5 A GUIDE TO SELECTING RANKING TECHNIQUES "Relevance Weighting of Search Terms." FRAKES, W. B. "On the Specification of Term Values in Automatic Indexing." "Index Term Weighting." Paper presented at ACM Conference on Research and Development in Information Retrieval, Pisa, Italy. LOCHBAUM, K. E., and L. A. STREETER. Harman and Candela (1990) experimented with various pruning algorithms using this method, looking for an algorithm that not only improved response time, but did not significantly hurt retrieval results. G. Salton and H. J. Schneider, pp. Instead it is a bucketed (10 slots/bucket) hash table that is accessed by hashing the query terms to find matching entries. "A Document Retrieval System Based on Nearest Neighbor Searching." where tqik = the ith term in the vector for query k This option allows a simple addition of each weight during the search process, rather than first multiplying by the IDF of the term, and provides very fast response time. As each query term is processed, its postings cause further additions to the accumulators. M. Williams, pp. For details on the search system associated with CITE, see section 14.7.2. "Probability and Fuzzy-Set Applications to Information Retrieval," in Annual Review of Information Science and Technology, ed. This hybrid dictionary is in alphabetic stem order, with the terms sorted within the stem, and contains the stem, the number of postings and IDF of the stem, the term, the number of postings and IDF of the term, a bit to indicate if the term is stemmed or not stemmed, and the offset of the postings for this stem/term combination. "Probability and Fuzzy-Set Applications to Information Retrieval," in Annual Review of Information Science and Technology, ed. where The use of the fixed block of storage to accumulate record weights that is described in the basic search process (section 14.6) becomes impossible for this huge data set. "Optimizing Convenient Online Access to Bibliographic Databases." "The Implementation of a Document Retrieval System," in Research and Development in Information Retrieval, eds. -------------------------------------------------------- Each of the following topics deals with a specific set of changes that need to be made in the basic indexing and/or search routines to allow the particular enhancement being discussed. If ranked output is wanted, the denominator of the cosine is computed from previously stored document lengths and the query length, and the records are sorted based on their similarity to the query. 5. "Construction of Weighted Term Profiles by Measuring Frequency and Specificity in Relevant Items." Their changed search algorithm with pruning is as follows: Many combinations of term-weighting can be done using the inner product. 1984. 1989), which is based on a two-stage search using signature files for a first cut and then ranking retrieved documents by term-weighting. 2. REFERENCES CLEVERDON, C. 1983. G. Salton and H. J. Schneider, pp. Figure 14.1: A simple illustration of statistical ranking In particular, they presented the following table showing the distribution of term t in relevant and nonrelevant documents for query q. An efficient file structure is used to record which query term appears in which given retrieved document. J. American Society for Information Science, in press. PERRY, S. A., and P. WILLETT. 1976. where HARTER, S. P. 1975. Documentation, 32(4), 294-317. A larger data set of 38,304 records had dictionaries on the order of 250,000 lines (250,000 unique terms, including some numerals) and an average of 88 postings per record. 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). Report from the School of Information Studies, Syracuse University, Syracuse, New York. They did experiments using all the relevance judgments to weight the terms to see what the optimal performance would be, and also used relevance judgments from half the collection to weight the terms for retrieval from the second half of the collection. Also, if a page includes the words ‘civil war’ in its title, that’s a hint that it might be more relevant than a document with the title ‘19th Century American Clothing.’ In the same way, if the words ‘civil war’ appear several times throughout the page, that page is more likely to be about the civil war than if the words only appear once [6]. 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. "Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, 24(5), 513-23. Documentation, 32(4), 294-317. CUTTING, D., and J. PEDERSEN. The following method serves only as an illustration of a very simple pruning procedure, with an example of the time savings that can be expected using a pruning technique on a large data set. 1976. Whereas the cosine similarity is used here with raw frequency term-weighting only (at least in the experiment described in Noreault, Koll and McGill [1977]), any of the term-weighting functions described in section 14.5 could be used. J. BELKIN, N. J. and W. B. CROFT. "A Statistical Interpretation of Term Specificity and Its Application in Retrieval." 1974. 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. "Retrieval Techniques," in Williams, M. Table 14.1:: Response Time wij = freqij X IDFi Documentation, 32(4), 294-317. Simulations of switches operating with these algorithms demonstrate different behavior of the switches for variable R and, in general, comparable performance with ROLM and improved performance over PIM and switches with deterministic schedulers [157]. SALTON, G., and C. BUCKLEY. Store the completely weighted term. The basic ranking search methodology described in the chapter is so fast that it is effective to use in situations requiring simple restrictions on natural language queries. "Computer Evaluation of Indexing and Text Processing." This chapter presents both a summary of past research done in the development of ranking algorithms and detailed instructions on implementing a ranking type of retrieval system. records retrieved First, the I/O needs to be minimized. 1980. Whereas this would solve the problem for smaller data sets, it creates a storage problem for the large data sets. 4. This process can be made much less dependent on the number of records retrieved by using a method developed by Doszkocs for CITE (Doszkocs 1982). Information Science, 6, 59-66. Documentation, 35(1), 30-48. MARON, M. E., and J. L. KUHNS. clustering using "nearest neighbor" techniques 1977. 1984. 1. (1983). LUHN, H. P. 1957. "A Document Retrieval System Based on Nearest Neighbor Searching." If ranked output is wanted, the denominator of the cosine is computed from previously stored document lengths and the query length, and the records are sorted based on their similarity to the query. 14.7.1 Handling Both Stemmed and Unstemmed Query Terms 1979. 2. Loading the necessary record statistics, such as record length, into memory before searching is essential to maintain any reasonable response time for this weighting option. Assuming within-document term frequencies are to be used, several methods can be used for combining these with the IDF measure. A simple extension of the basic search process in section 14.6 can be made that allows noncomplex Boolean statements to be handled (see section 14.8.4). Additionally, relevance feedback reweighting is difficult using this option. London: Butterworths. Information Science, 6, 59-66. The hybrid postings list saves the storage necessary for one copy of the record id by merging the stemmed and unstemmed weight (creating a postings element of 3 positions for stemmed terms). Berlin: Springer-Verlag. Paper presented at ACM Conference on Research and Development in Information Retrieval, Brussels, Belgium. Salton and Buckley suggest reducing the query weighting wiq to only the within-document frequency (freqiq) for long queries containing multiple occurrences of terms, and to use only binary weighting of documents (Wij = 1 or 0) for collections with short documents or collections using controlled vocabulary. "File Organization in Library Automation and Information Retrieval." 1978. 14.9 SUMMARY 1981. records retrieved Several operational retrieval systems have implemented ranking algorithms as central to their search mechanism. J. American Society for Information Science, 26(5), 280-89. Salton and Buckley suggest reducing the query weighting wiq to only the within-document frequency (freqiq) for long queries containing multiple occurrences of terms, and to use only binary weighting of documents (Wij = 1 or 0) for collections with short documents or collections using controlled vocabulary. "Optimizing Convenient Online Access to Bibliographic Databases." 14.7.2 Searching in Very Large Data Sets JARDINE, N., and C. J. 14.8.4 Use of Ranking in Two-level Search Schemes Figure 14.1: A simple illustration of statistical ranking Information Technology: Research and Development, 2(1), 1-21. It would be feasible to use structures other than simple inverted files, such as the more complex structures mentioned in that chapter, as long as the elements needed for ranking are provided. 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. J. It should be noted that, unlike section 14.6, some of the implementations discussed here should be used with caution as they are usually more experimental, and may have unknown problems or side effects. The indexing and retrieval were based on the singular value decomposition (related to factor analysis) of a term-document matrix from the entire document collection. BOOKSTEIN, A. -------------------------------------------------------- Association for Computing Machinery, 23(1), 76-88. "Search Term Relevance Weighting Given Little Relevance Information." 14.6.1 The Creation of an Inverted File This makes the searching process relatively independent of the number of retrieved records--only the sort for the final set of ranks is affected by the number of records being sorted. "Index Term Weighting." Loading the necessary record statistics, such as record length, into memory before searching is essential to maintain any reasonable response time for this weighting option. Table 14.1:: Response Time 1978. Each query term that is stemmed must now map to multiple dictionary entries, and postings lists must be handled more carefully as some terms have three elements in their postings list and some have only two. This implies that the file to be searched should be as short as possible, and for this reason the single file shown containing the terms, record ids, and frequencies is usually split into two pieces for searching: the dictionary containing the term, along with statistics about that term such as number of postings and IDF, and then a pointer to the location of the postings file for that term. The SIRE system (Noreault, Koll, and McGill 1977) incorporates a full Boolean capability with a variation of the basic search process. 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). After stemming, each term in the query is checked against the inverted file (this could be done by using the binary search described in section 14.6). It would be feasible to use structures other than simple inverted files, such as the more complex structures mentioned in that chapter, as long as the elements needed for ranking are provided. Whereas this would solve the problem for smaller data sets, it creates a storage problem for the large data sets. Average response time 0.28 0.58 1.1 1.6 In the area of stemming, a ranking system seems to work better by automatically expanding the query using stemming (Frakes 1984; Harman and Candela 1990) rather than by forcing the user to ask for expansion by wild-cards. "The Construction of a Thesaurus Automatically from a Sample of Text." SALTON, G., and M. E. LESK. 1983. Signature files have also been used in SIBRIS, an operational information retrieval system (Wade et al. Association for Computing Machinery, 15(1), 8-36. However, none of these schemes involve extensions to the basic search process in section 14.6. BOOKSTEIN, A. SPARCK JONES, K. 1981. 1984. freqij = the frequency of term i in document j Association for Computing Machinery, 24(3), 418-27. Improving Subject Retrieval in Online Catalogues, British Library Research Paper 24. SALTON, G. 1971. 14.3.4 Set-Oriented Ranking Models 1983. J. We present the complete architectural structure for an end-to-end efficient scalable VoD framework, simultaneously providing user personalization, reduced latency and operational costs. Relevance weighting is discussed further in Chapter 11 on relevance feedback. Number of queries 13 38 17 17 Some ranking experiments have relied more on document or intradocument structure than on the term-weighting described earlier. Paper presented at the Sixth International Conference on Research and Development in Information Retrieval, Bethesda, Maryland. Is returned as before, but also the “ quality ” of those.... Implemented at Syracuse University, Syracuse University, Syracuse University, Syracuse University ( NOREAULT et.. 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