: Addison-Wesley. User weighting can also be considered as additional weighting, although this type of weighting has generally proven unsatisfactory in the past. The response time for the 806 megabyte data set assumes parallel processing of the three parts of the data set, and would be longer if the data set could not be processed in parallel. Instead it is a bucketed (10 slots/bucket) hash table that is accessed by hashing the query terms to find matching entries. Additionally, relevance feedback reweighting is difficult using this option. In both cases, formula F4 was superior (closely followed by F3), with a large drop in performance between the optimal performance and the "predictive" performance, as would be expected. BURKOWSKI, F. J. This was the method chosen for the basic search process (see Figure 14.4). Paper presented at the Sixth International Conference on Research and Development in Information Retrieval, Bethesda, Maryland. "Automatic Ranked Output from Boolean Searches in SIRE." Several operational retrieval systems have implemented ranking algorithms as central to their search mechanism. 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. 117-51. The user may request ranked output. 1988. 4. "Experiments in Relevance Weighting of Search Terms." Documentation, 29(4), 351-72. ACM Transactions on Office Information Systems, 6(1), 42-62. Note that this combining of sets for complex Boolean queries can be a complicated operation. The list of ranked documents is returned as before, but only documents passing the added restriction are given to the user. "Construction of Weighted Term Profiles by Measuring Frequency and Specificity in Relevant Items." SALTON, G. 1971. The input query is processed similarly to a natural language query, except that the system notes the presence of special syntax denoting phrase limits or other field or proximity limitations. Signature files have also been used in SIBRIS, an operational information retrieval system (Wade et al. 14.8.4 Use of Ranking in Two-level Search Schemes. The user may request ranked output. This method is well described in Salton and Voorhees (1985) and in Chapter 15. A hybrid inverted file was devised to merge these files, saving no space in the dictionary part, but saving considerable storage over that needed to store two versions of the postings. 1984. 1. 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. Sort the accumulators with nonzero weights to produce the final ranked record list. "From Research to Application: The CITE Natural Language Information Retrieval System," in Research and Development in Information Retrieval, eds. 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). SALTON, G., and M. E. LESK. 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. The punishment for a website that tries to raise its PageRank in this way is that its PageRank is reduced to zero. If option 2 was used for weighting, then the weight stored in the postings is the normalized frequency of the stem in that record, and this needs to be multiplied by the IDF of that stem before the addition. In this manner the dictionary used in the binary search has only one "line" per unique term. CROFT, W. B., and L. RUGGLES. This was done in Croft's experimental re trieval system (Croft and Ruggles 1984). This same logic could be applied to the binary search of the dictionary, which takes about 14 reads per search for the larger data sets. Their inverted file consists of the dictionary containing the terms and pointers to the postings file, but the dictionary is not alphabetically sorted. Paper presented at ACM Conference on Research and Development in Information Retrieval, Brussels, Belgium. The basic inverted file creation and search process described in section 14.6 assumes a fairly static data set or a willingness to do frequent updates to the entire inverted file. Even a fast sort of thousands of records is very time consuming. 14.8.3 Ranking and Boolean Systems
Information Processing and Management, 15(3), 133-44. "Evaluation of the 2-Poisson Model as a Basis for Using Term Frequency Data in Searching." J. For example, a link to the Toyota Web site that said “car” would be considered relevant, but one that said “flowers” would be irrelevant. PERRY, S. A., and P. WILLETT. The various term-weighting schemes were not combined in this experiment. 1990. SALTON, G., and C. S. YANG. 1984. 14.7.1 Handling Both Stemmed and Unstemmed Query Terms
14.8.4 Use of Ranking in Two-level Search Schemes
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. The postings file contains the record ids and the weights for all occurrences of the term. "Precision Weighting -- An Effective Automatic Indexing Method." A Boolean query is processed in two steps. If the query term is not common, it is then passed through the stemming routine and a binary search for that stem is executed against the dictionary. This produces the slowest search (likely much too slow for large data sets), but the most flexible system in that term-weighting algorithms can be changed without changing the index. Table 14.1:: Response Time
Paper presented at the Statistical Association Methods for Mechanized Documentation. The best value for K proved to be 0.3 for the automatically indexed Cranfield collection, and 0.5 for the NPL collection, confirming that within-document term frequency plays a much smaller role in the NPL collection with its short documents having few repeating terms. "Surrogate Subsets: A Free Space Management Strategy for the Index of a Text Retrieval System."
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BUCKLEY, C., and A. LEWIT. 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). 14.8.4 Use of Ranking in Two-level Search Schemes
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. This extension, however, limits the Boolean capability and increases response time when using Boolean operators. DENNIS, S. F. 1964. Paper presented at ACM Conference on Research and Development in Information Retrieval, Pisa, Italy. Relevance Feedback in Document Retrieval Systems: An Evaluation of Probabilistic Strategies. 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. 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. Association for Computing Machinery, 23(1), 76-88. --------------------------------------------------------
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2. "Precision Weighting -- An Effective Automatic Indexing Method." BOOKSTEIN, A. "Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval." The advent of mobile devices and smartphones such as iPhones, iPads and others, has provided users with a wealth of applications and services anywhere, anytime. 1977. HARMAN, D., and G. CANDELA. 1976. For further details on clustering and its use in ranking systems, see Chapter 16. Association for Computing Machinery, 7(3), 216-44. where
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). ROBERTSON, S. E., and K. SPARCK JONES. (For algorithms to do efficient binary searches, see Knuth [1973], and for an alternative to binary searching see section 14.7.4.) (Ed. J. of Information Science, 6, 25-33. It is possible to provide ranking using signature files (for details on signature files, see Chapter 4 on that subject). 14.3.4 Set-Oriented Ranking Models
1989. J. "Retrieval Techniques," in Williams, M. where
J. Domain Age: In this video, Google’s Matt Cutts states that:In other words, they do use domain age…but it’s not very important.2. Information Storage and Retrieval, 9(11), 619-33. Formula F1 had been used by Barkla (1969) for relevance feedback in a SDI service and by Miller (1971) in devising a probabilistic search strategy for Medlars. per query
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). ACM Transactions on Office Information Systems, 6(1), 42-62. 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. DOSZKOCS, T. E. 1982. The combination recommended for most situations by Salton and Buckley is given below (a complete set of weighting schemes is presented in their 1988 paper). J. ni = the number of documents having term i in the data set
Of course the situation can be far more complex than the one shown: A page can link to another one, which itself can link back to the former. SALTON, G. 1971. 1977. A check needs to be made after step 1 for this. The term-weighting is done in the search process using the raw frequencies stored in the postings lists. 1973. This distribution model proved much less successful because of the difficulty in estimating the many parameters needed for implementation. 1971. BOOKSTEIN, A., and D. R. SWANSON. Relevance weighting is discussed further in Chapter 11 on relevance feedback. "On the Specification of Term Values in Automatic Indexing." SPARCK JONES, K. 1973. If the stem is found in the dictionary, the address of the postings list for that stem is returned, along with the corresponding IDF and the number of postings. CROFT, W. B. "Computer Evaluation of Indexing and Text Processing." 1984. The postings file contains the record ids and the weights for all occurrences of the term. In 1979 Croft and Harper published a paper detailing a series of experiments using probabilistic indexing without any relevance information. London: Butterworths. MARON, M. E., and J. L. KUHNS. 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. CROFT, W. B., and P. SAVINO. J. American Society for Information Science, 28(6), 333-39. 1988. This group included both the cosine correlation and the inner product function used in the probabilistic models. J. American Society for Information Science, 27(3), 129-46. : Addison-Wesley. 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. Figure 14.5: Merged dictionary and postings file
HARMAN, D. 1986. A running sum containing the numerator of the cosine similarity is updated by adding the new record frequencies, and this is continued until the entire Boolean query is processed. A commercial outgrowth of this system, marketed as Personal Librarian, uses ranking based on different factors, including the IDF and the frequency of a term within a document. "Construction of Weighted Term Profiles by Measuring Frequency and Specificity in Relevant Items." BUCKLEY, C., and A. LEWIT. Recent work on the effective use of inverted files suggests better ways of storing and searching these files (Burkowski 1990; Cutting and Pedersen 1990). Full-text indexing was used on various standard test collections, with full-text indexing also done on the queries. SALTON, G., and C. S. YANG. Information Services and Use, 4(1/2), 37-47.
Sort all query terms (stems) by decreasing IDF value. 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. NOREAULT, T., M. KOLL, and M. MCGILL. "An Experimental Study of Factors Important in Document Ranking." J. American Society for Information Science, 27(3), 129-46. RAGHAVAN, V. V., H. P. SHI, and C. T. YU. "Probabilistic Models for Automatic Indexing." "Computer Evaluation of Indexing and Text Processing." These end-users are likely to be familiar with the terminology of the data set they are searching, but lack the training and practice necessary to get consistently good results from a Boolean system because of the complex query syntax required by these systems. The ranking method would do well with this query. Sort all query terms (stems) by decreasing IDF value. "Testing of a Natural Language Retrieval System for a Full Text Knowledge Base." Examples of these types of restrictions would be requirements involving Boolean operators, proximity operators, special publication dates, specific authors, or the use of phrases instead of simple terms. The Art of Computer Programming, Reading, Mass. Formula F4 (minus the log) is the term precision weighting measure proposed by Yu and Salton (1976). 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. A major time bottleneck in the basic search process is the sort of the accumulators for large data sets. 1983. RAGHAVAN, V. V., H. P. SHI, and C. T. YU. 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. London: Butterworths. 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. "Testing of a Natural Language Retrieval System for a Full Text Knowledge Base." SRINIVASAN, P. 1989. This chapter has presented a survey of statistical ranking models and experiments, and detailed the actual implementation of a basic ranking retrieval system. In looking at results from all the experiments, some trends clearly emerge. "The Use of Hierarchic Clustering in Information Retrieval." This method is well described in Salton and Voorhees (1985) and in Chapter 15. --------------------------------------------------------
A very elaborate weighting scheme was devised for this experiment, tailored to the particular structure of the knowledge base. He used these to rank results from Boolean retrievals using both controlled (manually indexed) and uncontrolled (full-text) indexing. Information Science, 15, 249-60. 1989. Setting C to 1 ranks the documents by IDF weighting within number of matches, a method that was suitable for the manually indexed Cranfield collection used in this study (because it can be assumed that each matching query term was very significant). COOPER, W. S., and M. E. MARON. terms per query
14.4.2 Ranking Based on Document Structure
The test queries are those brought in by users during testing of a prototype ranking retrieval system. This method is well described in Salton and Voorhees (1985) and in Chapter 15. SPARCK JONES, K. 1981. 1978. 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. clustering using "nearest neighbor" techniques
Paper presented at the Sixth International Conference on Research and Development in Information Retrieval, Bethesda, Maryland. Information Processing and Management, 25(4), 347-61. J. 14.8 TOPICS RELATED TO RANKING
Information Processing and Management, 25(6), 665-76. ROBERTSON, S. E., and K. SPARCK JONES. lengthj = the number of unique terms in document j
(Ed. 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. 1988. J. FRAKES, W. B. ACM Transactions on Office Information Systems, 6(1), 42-62. Instead it is a bucketed (10 slots/bucket) hash table that is accessed by hashing the query terms to find matching entries. maxfreqj = the maximum frequency of any term in document j
"A Statistical Interpretation of Term Specificity and Its Application in Retrieval." Some search engines go a step further and take into account the importance of the page that a link comes from. Documentation, 35(4), 285-95. The major modification to the basic search process is to correctly merge postings from the query terms based on the Boolean logic in the query before ranking is done. There are four major options for storing weights in the postings file, each having advantages and disadvantages. Even a fast sort of thousands of records is very time consuming. 1. Store the completely weighted term. 14.7.4 Hashing into the Dictionary and Other Enhancements for Ease of Updating
1. An enhancement to the indexing program to allow easier updating is given in section 14.7.4. This was combined with weighting using both a function of term frequency within a document (the root mean square normalization), and a function of term frequency within the entire collection (the noise or entropy measure, or alternatively the IDF measure). 14.8.4 Use of Ranking in Two-level Search Schemes
"Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, 24(5), 513-23. "Operations Research Applied to Document Indexing and Retrieval Decisions." clustering using "nearest neighbor" techniques
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. HARPER, D. J. Report from the School of Information Studies, Syracuse University, Syracuse, New York. CROFT, W. B. Information Science, 6, 59-66. For further details on clustering and its use in ranking systems, see Chapter 16. As can be expected, the search process needs major modifications to handle these hybrid inverted files. Ranking retrieval systems have also been closely associated with clustering. As some terms have thousands of postings for large data sets, doing a separate read for each posting can be very time-consuming. In SIBRIS, an operational information retrieval system (Wade et al. Table 14.1 shows some timing results of this pruning algorithm. 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. per query (no pruning)
In both cases, formula F4 was superior (closely followed by F3), with a large drop in performance between the optimal performance and the "predictive" performance, as would be expected. "Operations Research Applied to Document Indexing and Retrieval Decisions." SALTON, G., and M. MCGILL. Documentation, 35(4), 285-95. BURKOWSKI, F. J. CROFT, W. B., and P. SAVINO. For more details see Doszkocs (1982). "From Research to Application: The CITE Natural Language Information Retrieval System," in Research and Development in Information Retrieval, eds. About hyphenation to create Indexing both in hyphenated and nonhyphenated form 6 ), 217-40 formula F4 ( the! Of document ranking algorithms links truth or does Google really use PigeonRank? 4, Ross Malaga... Be parsed into single terms and pointers to the postings records do not provide theoretical... Links, but the dictionary into memory when a data set only have the basic ranking search system a... 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