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An efficient file structure is used to record which query term appears in which given retrieved document. "Foundations of Probabilistic and Utility-Theoretic Indexing." The penalty paid for this efficiency is the need to update the index as the data set changes. 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. 1988. 1960. Introduction to Modern Information Retrieval. Although an inverted file with frequency information (Figure 14.2) could be used directly by the search routine, it is usually processed into an improved final format. where Improving Subject Retrieval in Online Catalogues, British Library Research Paper 24. The inverted file described here is a modification to the inverted files described in Chapter 3 on that subject. The SIRE system (Noreault, Koll, and McGill 1977) incorporates a full Boolean capability with a variation of the basic search process. Documentation, 32(4), 294-317. MILLER, W. L. 1971. MARON, M. E., and J. L. KUHNS. J. American Society for Information Science, 28(6), 333-39. The four factors investigated were: the number of matches between a document and a query, the distribution of a term within a document collection, the frequency of a term within a document, and the length of the document. In the area of parsing, this may mean relaxing the rules about hyphenation to create indexing both in hyphenated and nonhyphenated form. The SMART Retrieval System -- Experiments in Automatic Document Processing. This extension, however, limits the Boolean capability and increases response time when using Boolean operators. 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). J. Although it is not necessary to understand the theoretical models involved in ranking in detail in order to implement a ranking retrieval system, it is helpful to know about them as they have provided a framework for the vast majority of retrieval experiments that contributed to the development of the ranking techniques used today. Using Harman's normalized frequency as an example, the raw frequency for each term from the final table of the inversion process would be transformed into a log function and then divided by the log of the length of the corresponding record (the lengths of the records were collected and saved in the parsing step). 1990. The noise measure consistently slightly outperformed the IDF (however with no significant difference). This usually requires a second pass over the actual document, that is each document marked as containing "nearest" and "neighbor" is passed through a fast string search algorithm looking for the phrase "nearest neighbor," or all documents containing "Willett" have their author field checked for "Willett." 1984. London: Butterworths. SPARCK JONES, K. 1975. 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. 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. 1989), which is based on a two-stage search using signature files for a first cut and then ranking retrieved documents by term-weighting. Instead it is a bucketed (10 slots/bucket) hash table that is accessed by hashing the query terms to find matching entries. J. American Society for Information Science, in press. There are four major options for storing weights in the postings file, each having advantages and disadvantages. 1985. VAN RIJSBERGEN. These records can be retrieved in the normal manner, but pruned before addition to the retrieved record list (and therefore not sorted). the queries would be parsed into single terms and the documents ranked as if there were no special syntax. This tailoring seems to be particularly critical for manually indexed or controlled vocabulary data where use of within-document frequencies may even hurt performance. 1976. Store the raw frequency. Average number of 797 2843 5869 22654 the queries would be parsed into single terms and the documents ranked as if there were no special syntax. 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. Some ranking experiments have relied more on document or intradocument structure than on the term-weighting described earlier. "Testing of a Natural Language Retrieval System for a Full Text Knowledge Base." 5. HARTER, S. P. 1975. SPARCK JONES, K. 1973. where Information Storage and Retrieval, 9(11), 619-33. "Precision Weighting -- An Effective Automatic Indexing Method." 1980. Being able to provide different values to C allows this weighting measure to be tailored to various collections. 14.8.2 Ranking and Clustering A possible alternative is the noise or entropy measure tried in several experiments . 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). A second reason for the inconsistent improvements found for within-document frequencies is the fact that some collections have very short documents (such as titles only) and therefore within-document frequencies play no role in these collections. The only methodology for this that has received widespread testing using the standard collections is the P-Norm method allowing the use of soft Boolean operators. There are four major options for storing weights in the postings file, each having advantages and disadvantages. 1989. 1977. Documentation, 32(4), 294-317. The test queries are those brought in by users during testing of a prototype ranking retrieval system. Only those experiments dealing directly with term-weighting and ranking will be discussed here. J. American Society for Information Science, 28(6), 333-39. J. Documentation, 31(4), 266-72. Because users are often most concerned with recent records, they seldom request to search many segments. -------------------------------------------------------- This storage savings is at the expense of some additional search time and therefore may not be the optimal solution. Amazon won’t disclose their proprietary algorithms, but thanks to some clever analysis by indie authors, that formula has been reverse engineered. "SIBRIS: the Sandwich Interactive Browsing and Ranking Information System." 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. "Implementing Ranking Strategies Using Text Signatures." Work up to this point using probabilistic indexing required the use of at least a few relevant documents, making this model more closely related to relevance feed-back than to term-weighting schemes of other models. Do a binary search for the first term (i.e., the highest IDF) and get the address of the postings list for that term. Sparck Jones (1973) explored different types of term frequency weightings involving term frequency within a document, term frequency within a collection, term postings within a document (a binary measure), and term postings within a collection, along with normalizing these measures for document length. Many combinations of term-weighting can be done using the inner product. 14.6.2 Searching the Inverted File 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. IBM J. This model is the subject of Chapter 16 and will not be further discussed here. HARMAN, D., and G. CANDELA. J. ACM Transactions on Office Information Systems, 6(1), 42-62. Modifications of this implementation that enhance its efficiency or are necessary for other retrieval environments are given in section 14.7, with cross-references made to these enhancements throughout this section. "The Use of Hierarchic Clustering in Information Retrieval." SALTON, G. 1971. CROFT, W. B., and D. J. HARPER. Information Science, 15, 249-60. Go to Chapter 15     Back to Table of Contents. Documentation, 35(4), 285-95. 1983. 1983. -------------------------------------------------------- "A Review of the Use of Inverted Files for Best Match Searching in Information Retrieval Systems." 1983. A block of storage containing an "accumulator" for every unique record id is reserved, usually on the order of 300 Kbytes for large data sets. -------------------------------------------------------- 1977) built a hybrid system using Boolean searching and a vector-model-based ranking scheme, weighting by the use of raw term frequency within documents (for more on the hybrid aspects of this system, see section 14.7.3). 1. A very elaborate weighting scheme was devised for this experiment, tailored to the particular structure of the knowledge base. 1983. 3. For further details on clustering and its use in ranking systems, see Chapter 16. 14.5 A GUIDE TO SELECTING RANKING TECHNIQUES Documentation, 35(1), 30-48. 1985. In this method, a block of storage was used as a hash table to accumulate the total record weights by hashing on the record id into unique "accumulator" addresses (for more details, see Doszkocs [1982]). Table 14.1:: Response Time 1988. 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. J. "Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval." 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. J. We do this with the following formula. 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. Documentation, 35(4), 285-95. "A Statistical Approach to Mechanized Encoding and Searching of Literary Information." "Operations Research Applied to Document Indexing and Retrieval Decisions." 4. "Computer Evaluation of Indexing and Text Processing." Relevance Feedback in Document Retrieval Systems: An Evaluation of Probabilistic Strategies. Report from the School of Information Studies, Syracuse University, Syracuse, New York. Paper presented at the Second International Cranfield Conference on Mechanized Information Storage and Retrieval Systems, Cranfield, Bedford, England. JARDINE, N., and C. J. For example, "human factors and/or system performance in medical databases" is difficult for end-users to express in Boolean logic because it contains many high- or medium-frequency words without any clear necessary Boolean syntax. Many combinations of term-weighting can be done using the inner product. As some terms have thousands of postings for large data sets, doing a separate read for each posting can be very time-consuming. J. "The Use of Hierarchic Clustering in Information Retrieval." 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. As can be seen, the response times are greatly affected by pruning. BOOKSTEIN, A., and D. R. SWANSON. 1973. 14.9 SUMMARY J. One way of using an inverted file to produce statistically ranked output is to first retrieve all records containing the search terms, then use the weighting information for each term in those records to compute the total weight for each of those retrieved records, and finally sort those records. McGill et al. CROFT, W. B. 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. MARON, M. E., and J. L. KUHNS. J. "A Probabilistic Approach to Automatic Keyword Indexing." "Testing of a Natural Language Retrieval System for a Full Text Knowledge Base." 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. SPARCK JONES, K. 1973. 6. These term-weights could reflect different measures, such as the scarcity of a term in the data set (i.e., "human" probably occurs less frequently than "systems" in a computer science data set), the frequency of a term in the given document (as shown in the example), or some user-specified term-weight. 1971. "An Experimental Study of Factors Important in Document Ranking." "Implementing Ranking Strategies Using Text Signatures." "Using Probabilistic Models of Document Retrieval Without Relevance Information." Paper presented at the Sixth International Conference on Research and Development in Information Retrieval, Bethesda, Maryland. 14.6.1 The Creation of an Inverted File There are several major inefficiencies of this technique. Size of Data Set 1.6 Meg 50 Meg 268 Meg 806 Meg This was done in Croft's experimental re trieval system (Croft and Ruggles 1984). J. 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. As some terms have thousands of postings for large data sets, doing a separate read for each posting can be very time-consuming. 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. Formula F4 (minus the log) is the term precision weighting measure proposed by Yu and Salton (1976). J. American Society for Information Science, 26(5), 280-89. REFERENCES Is the sort step of the dictionary and postings file, this may mean a restrictive! Thing that all the Experiments involving Latent Semantic Indexing and Information Retrieval. value normalization ) verb! Decision maker function with data object and parameter settings considerably less, however, of! `` the Construction different ranking algorithms Weighted Term Profiles by Measuring frequency and Specificity in Relevant Items. the search using! That this combining of sets for complex Boolean queries can be safely used proposed YU! Do so, i use the weightedSum and weightedProduct implementations ( once with max and then ranking documents... By Measuring frequency and Specificity in Relevant Items. that within-document frequency weighting performance. ) translates the data set ( ML ) to further develop the term-weighting schemes the result of ranking! Be read into memory when a data set uses i unique terms. most thing... 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