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. SALTON, G., and C. BUCKLEY. Because users are often most concerned with recent records, they seldom request to search many segments. These records can be retrieved in the normal manner, but pruned before addition to the retrieved record list (and therefore not sorted). RankBrain: RankBrain is Google’s AI algorithm. "A Statistical Interpretation of Term Specificity and Its Application in Retrieval." This is not a major factor for small data sets and for some retrieval environments, especially those involved in research into new retrieval mechanisms. Although this seems a tedious method of handling phrases or field restrictions, it can be done in parallel with user browsing operations so that users are often unaware that a second processing step is occurring. There are several major inefficiencies of this technique. Do a binary search for the first term (i.e., the highest IDF) and get the address of the postings list for that term. 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. 1977. "The Use of Hierarchic Clustering in Information Retrieval." FRAKES, W. B. So you have a list of N cars with their price information. The subsetting or segmenting is done in reverse chronological order. The list of ranked documents is returned as before, but only documents passing the added restriction are given to the user. The following technique was developed for the prototype retrieval system described in Harman and Candela (1990) to handle this problem, but it is not thought to be an optimal method. "Construction of Weighted Term Profiles by Measuring Frequency and Specificity in Relevant Items." 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. "Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, 24(5), 513-23. "The Use of Hierarchic Clustering in Information Retrieval." In SIBRIS, an operational information retrieval system (Wade et al. COOPER, W. S., and M. E. MARON. "Experiments in Relevance Weighting of Search Terms." For smaller data sets, or for environments where ease of update and flexibility are more important than query response time, the inverted file could have a structure more conducive to updating. Information Storage and Retrieval, 7(5), 217-40. "Optimizations for Dynamic Inverted Index Maintenance." : Addison-Wesley. M. Williams, pp. 14.7.4 Hashing into the Dictionary and Other Enhancements for Ease of Updating
BERNSTEIN, L. M., and R. E. WILLIAMSON. The SIRE system (Noreault, Koll, and McGill 1977) incorporates a full Boolean capability with a variation of the basic search process. Documentation, 29(4), 351-72. 14.9 SUMMARY
Number of queries 13 38 17 17
"Using Probabilistic Models of Document Retrieval Without Relevance Information." Q = the number of matching terms between document j and query k
There are many possible modifications and enhancements to the basic indexing and search processes, some of which are necessary because of special retrieval environments (those involving large and very large data sets are discussed), and some of which are techniques for enhancing response time or improving ease of updating. DOSZKOCS, T. E. 1982. "A Probabilistic Approach to Automatic Keyword Indexing." M. Williams, pp. 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. Because users are often most concerned with recent records, they seldom request to search many segments. The list of ranked documents is returned as before, but only documents passing the added restriction are given to the user. Association for Computing Machinery, 24(3), 418-27. Various methods have been developed for dealing with this problem. Paper presented at the Second International Cranfield Conference on Mechanized Information Storage and Retrieval Systems, Cranfield, Bedford, England. In some cases, however, a stem is produced that leads to improper results, causing query failure. SALTON, G., and M. E. LESK. HARPER, D. J. Table 14.1:: Response Time
Documentation, 31(4), 266-72. Figure 14.3: A dictionary and postings file
"Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval." 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. The record ids and raw frequencies for the term being processed are combined with those of the previous set of terms according to the appropriate Boolean logic. RankBrain: RankBrain is Google’s AI algorithm. Association for Computing Machinery, 24(3), 418-27. 1971. The test queries are those brought in by users during testing of a prototype ranking retrieval system. Query terms would normally use the stemmed version, but query terms marked with a "don't stem" character would be routed to the unstemmed version. Association for Computing Machinery, 25(1), 67-80. maxnoise = the highest noise of any term in the collection
2. Information Science, 6, 59-66. 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. "Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, 24(5), 513-23. It was also suggested that clustering could improve the performance of retrieval by pregrouping like documents (Jardine and van Rijsbergen 1971). 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. 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). There are no modifications to the basic inverted file needed unless adjacency, field restrictions, and other such types of Boolean operations are desired. TFreqi = the total frequency of term i in the collection
This system therefore is much more flexible and much easier to update than the basic inverted file and search process described in section 14.6. G. Salton and H. J. Schneider, pp. Combining the within-document frequency with either the IDF or noise measure, and normalizing for document length improved results more than twice as much as using the IDF or noise alone in the Cranfield collection. In looking at results from all the experiments, some trends clearly emerge. This extension, however, limits the Boolean capability and increases response time when using Boolean operators. 1989), which is based on a two-stage search using signature files for a first cut and then ranking retrieved documents by term-weighting. SALTON, G., and M. E. LESK. 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. "Operations Research Applied to Document Indexing and Retrieval Decisions." Documentation, 35(4), 285-95. "Experiments with Representation in a Document Retrieval System." "Precision Weighting -- An Effective Automatic Indexing Method." 1. 14.3.3 Other Models for Ranking Individual Documents
SALTON, G., and M. E. LESK. records retrieved
The test queries are those brought in by users during testing of a prototype ranking retrieval system. They then use this table to derive four formulas that reflect the relative distribution of terms in the relevant and nonrelevant documents, and propose that these formulas be used for term-weighting (the logs are related to actual use of the formulas in term-weighting).
M. Williams, pp. Clearly, for data sets that are relatively small it is best to use the two separate inverted files because the storage savings are not large enough to justify the additional complexity in indexing and searching. Very elaborate schemes have been devised that combine Boolean with ranking, and references are made to these in section 14.8.3. The term-weighting results were more mixed, with no significant difference found when using controlled vocabulary (i.e., term-weighting made no difference) and an overall significant difference found for uncontrolled vocabulary. LOCHBAUM, K. E., and L. A. STREETER. 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. 1977. "Experiments in Relevance Weighting of Search Terms." "Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval." CROFT, W. B., and P. SAVINO. --------------------------------------------------------
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