| { |
| "paper_id": "C69-0401", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T12:32:25.136037Z" |
| }, |
| "title": "Preprint No. 4 Classification: IR 2.3 Automatic Processing of Foreign Language Documents", |
| "authors": [ |
| { |
| "first": "G", |
| "middle": [], |
| "last": "Salton", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Experiments conducted over the last few years with the SMART document retrieval system have shown that fully automatic text processing methods using relatively simple linguistic tools are as effective for purposes of document indexing, classification, search, and retrieval as the more elaborate manual methods normally used in practice. Up to now, all experiments were carried out entirely with English language queries and documents. The present study describes an extension of the SMAKT procedures to German language materials. A multilingual thesaurus is used for the analysis of documents and search requests, and tools are provided which make it possible to process English language documents against German queries, and vice versa. The methods are evaluated, and it is shown that the effectiveness of the mixed language processing is approximately equivalent to that of the standard process operating within a single language only.", |
| "pdf_parse": { |
| "paper_id": "C69-0401", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Experiments conducted over the last few years with the SMART document retrieval system have shown that fully automatic text processing methods using relatively simple linguistic tools are as effective for purposes of document indexing, classification, search, and retrieval as the more elaborate manual methods normally used in practice. Up to now, all experiments were carried out entirely with English language queries and documents. The present study describes an extension of the SMAKT procedures to German language materials. A multilingual thesaurus is used for the analysis of documents and search requests, and tools are provided which make it possible to process English language documents against German queries, and vice versa. The methods are evaluated, and it is shown that the effectiveness of the mixed language processing is approximately equivalent to that of the standard process operating within a single language only.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "One of the major objections to the praetical utilization of the automatic text processing methods has been the inability automatically to handle foreign language texts of the kind normally stored in documentation and library systems. Recent experiments performed with document abstracts and search requests in French and German appear to indicate that these objections may be groundless.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "In the present study~ the SMART documsnt retrieval system is used to carry out experlments using as input foreign language documents and queries. The foreign language texts are automatically processed using a thesaurus (synonym dictionary) translated directly from a previously available English version. Foreign language query and document texts are lookedup in the foreign language thesaurus and the analyzed forms of the queries and documents are then compared in the standard manner before retrieving the highly matching items. The language analysis methods incorporated into the SMART system are first briefly reviewed. Thereafter, the main procedures used to process the foreign language documents are described, and the retrieval effectiveness of the English text processing methods is compared with that of the foreign language material.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "SMART is a fully-automatic document retrieval system operating on the IBM 7094 and 360 model 65. Unlike other computer-based retrieval systems, SMART is thus designed as an experimental automatic retrieval system of the kind that may become current in operational environments some years hence.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The SMART System", |
| "sec_num": "2." |
| }, |
| { |
| "text": "The following facilities, incorporated into the SMART system for purposes of document analysis may be of principal interest: a) a system for separating English words into stems and affixes (the so-called suffix 's' and stem thesaurus methods) which can be used to construct document identifications consisting of the stems of words contained in the documents; b) a synonym dictionary, or thesaurus, which can be used to recognize synonyms by replacing each word stem by one or more \"concept\" numbers; these concept numbers then serve as content identifiers instead of the original word stems; c) a hierarchical arrangement of the concepts included in the thesaurus which makes it possible, given any concept number, to find its \"parents\" in the hierarchy, its \"sons\", its g) a dictionary u~datln~ system, designed to revise the several dictionaries included in the system: i) word stem dictionary ii)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The SMART System", |
| "sec_num": "2." |
| }, |
| { |
| "text": "word suffix dictionary iii) common word dictionary (for words to be deleted duping analysis) iv) thesaurus (synonym dictionary) v) concept hierarchy vi) statistical phrase dictionary vii) syntactic (\"criterion\") phmase dictionary.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The SMART System", |
| "sec_num": "2." |
| }, |
| { |
| "text": "The operations of the system are built around a supemvisory system which decodes the input instructions and arranges the processing sequence in accordance with the instructions received. The SMART systems organization makes it possible to evaluate the effectiveness of the various processing methods by comparing the outputs produced by a variety of different runs. This is achieved by processing the same search requests against the same document collections several times, and making judicious changes in ~e analysis procedures between runs. In each case, the search effectiveness is evaluated by presenting paired comparisons of the average perfommance over many search requests for two given search and retrieval methodologies. A typical thesaurus excerpt is shown in Fig. 3 Query QB 13 in Three Languages Fig. 4 into German by a native German speaker.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 772, |
| "end": 778, |
| "text": "Fig. 3", |
| "ref_id": "FIGREF5" |
| }, |
| { |
| "start": 810, |
| "end": 816, |
| "text": "Fig. 4", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "The SMART System", |
| "sec_num": "2." |
| }, |
| { |
| "text": "The English queries were then processed against both the English and the German collections (runs E-E and E-G), and the same was done for the translated German queries (runs G-E and G-G, respectively). Relevance assessments were made for each English document abstract with respect to each English query by a set of eight American students in library science, and the assessors were not identical to the users who originally submitted the search requests. language analysis is summarized in Table 3 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 491, |
| "end": 498, |
| "text": "Table 3", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "The SMART System", |
| "sec_num": "2." |
| }, |
| { |
| "text": "Since the query processing operates equally well in both languages, while the German document collection produces a degraded performance, it becomes worthwhile to examine the principal differences between the two document collections. These are summarized in Table 4 The other thesaurus characteristic -that is its completenessappears to present a more serious problem. Table 4 shows that only approx- .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 259, |
| "end": 266, |
| "text": "Table 4", |
| "ref_id": null |
| }, |
| { |
| "start": 370, |
| "end": 377, |
| "text": "Table 4", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Failure Analysis", |
| "sec_num": "6." |
| }, |
| { |
| "text": "-27to produce a document content analysis which is equally effective in English as in German.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ".-I", |
| "sec_num": null |
| }, |
| { |
| "text": "In particular, differences in morphology (for example, in the suffix cut-off rules], and in language ambiguities do not seem to cause a substantial degradation when moving from one language to another.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ".-I", |
| "sec_num": null |
| }, |
| { |
| "text": "For these reasons, the automatic retrieval methods used in the SMART system for English appear to be applicable also to foreign language material.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ".-I", |
| "sec_num": null |
| }, |
| { |
| "text": "Future experiments with foreign language documents should be carried out using a thesaurus that is reasonably complete in all languages, and with identical query and document collections for which the same relevance judgments may then be applicable across all runs.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ".-I", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "Characteristics Table 4 ", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 16, |
| "end": 23, |
| "text": "Table 4", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Document Collection", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "The SMART Automatic Document Retrieval System -An Illustration", |
| "authors": [ |
| { |
| "first": "M", |
| "middle": [ |
| "E" |
| ], |
| "last": "Salton", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Lesk", |
| "suffix": "" |
| } |
| ], |
| "year": 1965, |
| "venue": "Communications of the ACM", |
| "volume": "8", |
| "issue": "6", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "References [i~ G. Salton and M. E. Lesk, The SMART Automatic Document Retrieval System -An Illustration, Communications of the ACM, Vol. 8, No. 6, June 1965.", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Automatic Information Organization and Retrieval", |
| "authors": [ |
| { |
| "first": "G", |
| "middle": [], |
| "last": "Salton", |
| "suffix": "" |
| } |
| ], |
| "year": 1968, |
| "venue": "", |
| "volume": "514", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "G. Salton, Automatic Information Organization and Retrieval, McGraw Hill Book Company, New York, 1968, 514 pages.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "Computer Evaluation of Indexing and Text Processing", |
| "authors": [ |
| { |
| "first": "G", |
| "middle": [], |
| "last": "Salton", |
| "suffix": "" |
| }, |
| { |
| "first": "M", |
| "middle": [ |
| "E" |
| ], |
| "last": "Lesk", |
| "suffix": "" |
| } |
| ], |
| "year": 1968, |
| "venue": "Journal of the ACM", |
| "volume": "15", |
| "issue": "i", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "G. Salton and M. E. Lesk, Computer Evaluation of Indexing and Text Processing, Journal of the ACM, Vol. 15, No. i, January 1968.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "Factors Determining the Performance of Indexing Systems", |
| "authors": [ |
| { |
| "first": "C", |
| "middle": [ |
| "W" |
| ], |
| "last": "Cleverdon", |
| "suffix": "" |
| }, |
| { |
| "first": "E", |
| "middle": [ |
| "M" |
| ], |
| "last": "Keen", |
| "suffix": "" |
| } |
| ], |
| "year": 1966, |
| "venue": "", |
| "volume": "2", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "C. W. Cleverdon and E. M. Keen, Factors Determining the Performance of Indexing Systems, Vol. i: Design, Vol. 2: Test Results, Aslib Cranfield Research Project, Cranfield, England, 1966.", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "A Comparison Between Manual and Automatic Indexing Methods, American Documentation", |
| "authors": [ |
| { |
| "first": "G", |
| "middle": [], |
| "last": "Salton", |
| "suffix": "" |
| } |
| ], |
| "year": 1969, |
| "venue": "", |
| "volume": "20", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "G. Salton, A Comparison Between Manual and Automatic Indexing Methods, American Documentation, Vol. 20, No. i, January 1969.", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "Evaluation of the Operating Efficiency of Medlars", |
| "authors": [ |
| { |
| "first": "F", |
| "middle": [ |
| "W" |
| ], |
| "last": "Lancaster", |
| "suffix": "" |
| } |
| ], |
| "year": 1969, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "F. W. Lancaster, Evaluation of the Operating Efficiency of Medlars, Final Report, National Library of Medicine, Washington, January 1969.", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "Computer Classification of Documents, FID-IFIP Conference on Mechanized Documentation", |
| "authors": [ |
| { |
| "first": "J", |
| "middle": [ |
| "H" |
| ], |
| "last": "Williams", |
| "suffix": "" |
| } |
| ], |
| "year": 1967, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "J. H. Williams, Computer Classification of Documents, FID-IFIP Conference on Mechanized Documentation, Rome, June 1967.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "Relevance Assessments and Retrieval System Evaluation", |
| "authors": [ |
| { |
| "first": "M", |
| "middle": [ |
| "E" |
| ], |
| "last": "Lesk", |
| "suffix": "" |
| }, |
| { |
| "first": "G", |
| "middle": [], |
| "last": "Salton", |
| "suffix": "" |
| } |
| ], |
| "year": 1968, |
| "venue": "Information Storage and Retrieval", |
| "volume": "4", |
| "issue": "4", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "M. E. Lesk and G. Salton, Relevance Assessments and Retrieval System Evaluation, Information Storage and Retrieval, Vol. 4~ No. 4, October 1968.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "num": null, |
| "text": "some years, experiments have been under way to test the effectiveness of automatic language analysis and indexing methods in information retrieval, Specifically, document and query texts are processed fully automatically, and content identifiers are assigned using a variety of linguistic ~Department of Computer Science, Cornell University, Ithaca, N. Y. 14850. This study was supported in part by the National Science Foundation under grant GN-750. tools, including word stem analysis, thesaurus look-up, phrase recognition, statistical term association~ syntactic analysis, and so on. The resulting concept identifiers assigned to each document and search request are then matched, and the documents whose identifiers are sufficiently close to the queries are retrieved for the user's attention. The automatic analysis methods can be made to operate in real-time -while the customer waits for an answer _ by restricting the query-document comparisons to only certain document classes, and interactive user-controlled search methods can be implemented which adjust the search request during the search in such a way that more useful, and less useless, material is retrieved from the file. The experimental evidence accumulated over the last few years indicates that retrieval systems based on automatic text processing methods -including fully automatic content analysis as well as automatic document classification and retrieval --are not in general inferior in retrieval effectiveness to conventional systems based on human indexing and human query formulation.", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF1": { |
| "num": null, |
| "text": "the SMART system does not rely on manually assigned key words or index terms for the identification of documents and search requests, nor does it use primarily the frequency of occurrence of certain words or phrases included in the texts of documents. Instead, an attempt is made to go beyond simple word-matchlng procedures by using a variety of intellectual aids in the form of synonym dictionaries, hierarchical arrangements of subject identifiers, statistical and syntactic phrase generation methods and the like, in order to obtain the content identifications useful for the retrieval process. Stored documents and search requests are then processed without any prior manual analy~i__sby one of several hundred automatic content analysis methods, and those documents which most nearly match a given search request are extracted from the document file in answer to the request. The system may be controlled by the use~, in that a search request can be processed first in a standard mode; the user can then analyze the output obtained and, depending on his further requirements, order a reproeessing of the request under new conditions. The new output can again be examined and the process iterated until the right kind and amount of information are retrieved. [1,2,3]", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "num": null, |
| "text": "Many different criteria may suggest themselves for measuring the performance of an information system.In the evaluation work carried out with the SMART system, the effectiveness of an information system is assumed to depend on its ability to satisfy the users' information needs by retrieving wanted material, while rejecting unwanted items.Two measures have been widely used for this purpose, known as recall and precision, and representing respectively the proportion of relevant material actually retrieved, and the proportion of retrieved material actually relevant. [3] (Ideally, all relevant items should be retrieved, while at the same time, all nonrelevant items should be rejected, as reflected by perfect recall and precision values equal to i). It should be noted that both the recall and precision figures achievable by a given system are adjustable, in the sense that a relaxation of the search conditions often leads to high recall, while a tightening of the search criteria leads to high precision. Unhappily, experience has shown that on the average recall and precision tend to vary inversely since the retrieval of more relevant items normally also leads to the retrieval of more irrelevant ones. In practice, a compromise is usually made, and a per-for~nance level is chosen such that much of the relevant material is retrieved, while the number of nonrelevant items which are also retrieved is kept within tolerable limits. In theory, one might expect that the performance of a retrieval sys-I tem would improve as the language analysis methods used for document and query processing become more sophisticated. In actual fact, this turns out not to be the case. A first indication of the fact that retrieval effec--7tiveness does not vary directly with the complexity of the document or query analysis was provided by the output of the Asllb-Cranfield studies. This project tested a large variety of indexing languages in a retrieval environment, and came to the astonishing conclusion that the simplest type of indexing language would produce the best results. [4] Specifically, three types of indexing languages were tested, called respectively single terms (that is, individual terms, or concepts assigned to documents and queries), controlled terms (that is, single terms assigned under the control of the well-known EJC Thesaurus of Engineering and Scientific Terms), and finally simple conce~ts (that is, phrases consisting of two or more single terms). The results of the Cranfield tests indicated that single terms are more effective for retrieval purposes than either controlled terms, or complete phrases. [4]These results might be dismissed as being due to certain peculiar test conditions if it were not for the fact that the results obtained with the automatic SMART retrieval system substantially confirqn the earlier Cran-field output. [3] Specifically, the following basic conclusions can be drawn from the main SMART experiments: a) the simplest automatic language analysis procedure consisting of the assignment to queries and documents of weighted word stems originally contained in these documents, produces a retrieval effectiveness almost equivalent to that obtained by intellectual indexing carried out manually under controlled conditions; [3,5] b) use of a thesaurus look-up process, designed to recognize synonyms and other term relations by repla<~ing the original word stems by the corresponding thesaurus categories, improves the retrieval effectiveness by about ten percent in both recall and -8precision; c) additional, more sophisticated language analysis procedures, including the assignment of phrases instead of individual terms, the use of a concept hierarchy, the determination of syntactic relations between terms, and so on, do not, on the average, provide improvements over the standard thesaurus process. An example of a typical recall-precision graph produced by the SMART system is shown in Fig. i, where a statistical phrase method is compared with a syntactic phrase procedure. In the former case, phrases are assigned as content identifiers to documents and queries whenever the individual phrase components are all present within a given document; in the latter case, the individual components must also exhibit an appropriate syntactic relationship before the phrase is assigned as an identifier. The output of Fig.l shows that the use of syntax degrades performance (the ideal perfor~nance region is in the upper right-hand corner of the graph where both the recall and the precision are close to i). Several arguments may explain the output of Fig. i: a) the inadequacy of the syntactic analyzer used to generate syntactic phrases; b) the fact that phrases are often appropriate content identifiers even when the phrase components are not syntactically related in a given context (e.g. the sentence \"people who need information, require adequate retrieval services\" is adequately identified by the phrase \"information retrieval\", even though the components are not related in the sentence); c) the variability of the user population which makes it unwise to overspecify document content; d) the ambiguity inherent in natural language texts which may work to advantage when attempting to satisfy the information needs of a heterogeneous user population with diverse infor-", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF3": { |
| "num": null, |
| "text": "thesaurus in which one concept category corresponds both to a family of English words, or word stems, as well as to their German translation.", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF4": { |
| "num": null, |
| "text": "The German relevance assessments (German documents against German queries), on the other hand, were obtained from a different, German speaking, assessor. The principal evaluation results for the four runs using the thesaurus process are shown in Fig. 5, averaged over 48 queries in each case. It is clear from the output of Fig. 5 that the cross-language runs, E-G (English queries -German documents} and G-E (German queries -English documents), are not substantially inferior to the corresponding output within a single language (G-G and E-E, respectively), the difference being of the order of 0.02 to 0.03 for a given recall level. On the other hand, both runs using the German document collection are inferior to the runs with the English collection. The output of Fig. 5 leads to the following principal conclusions: a) the query processing is comparable in both languages; for if this were not the case, then one would expect one set of query runs to be much less effective than the other (that is, either E-E and E-G, or else G-G and G-El; b) the language processing methods (that is, thesaurus categories, suffix cut-off procedures, etc.) are equally effective in both cases; if this were not the case, one would expect one of the single language runs to come out very poorly, but t.. ~0 t'-m ~ E, nor G-G came out as the poorest run; the cross-language runs are performed properly, for if this were not the cased one would expect E-G and G-E to perform much less well than the runs within a single language; since this is not the case, the principal conclusion is then obvious that documents in one language can be matched against queries in.~nothe F nearl [ as well a 9 documents a~d ~ue~ies in a single language; 'the runs using the German document collection (E-G and G-G) are less effective than those performed with the English collection; the indication is then apparent that some characteristic connected with the German document collection itself -for example, the type of abstract, or the language of the abstract, or the relevance assessments -requires improvement; the effectiveness of the cross-language processing, however, is not at issue.", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF5": { |
| "num": null, |
| "text": ". The following principal distinctions arise: a) the organization of the thesaurus used to group words or word stems into thesaurus categories; b) the completeness of the thesaurus in terms of words included in it; c) the type of document abstracts included in the collection; and E-G much better than G-E and G-G, or vice-versa Either E-E or G-G much poorer than cross-language runs Both E-G and G-E poorer than other runs Either E-G and G-G, or else G-E and E-E simul-: English-quePies -English documents E-G: English queries -German documents G-E: German queries -English documents G-G: German queries -Get, nan documents Analysis of Foreign Language ProcessingTable )the accuracy of the relevance assessments obtained from the collections.Concerning first the organization of the multi-lingual thesaurus, it does not appear that any essential difficulties arise on that account. This is confirmed by the fact that the cross-language runs operate satisfactorily, and by the output ofFig. 6 (a)comparing a German word stem run (using standard suffix cut-off and weighting procedures~ with a German thesaurus run.It is seen that the German thesaurus improves performance over word stems for the German collection in the same way as the English thesaurus was seen earlier to improve retrieval effectiveness over the English word stem analysis.[2,3]", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF6": { |
| "num": null, |
| "text": "English words per document abstract were not included in the English thesaurus, whereas over 15 words per abstract were missing from the German thesaurus. Obviously, if the missing words turn out to be impe~;tant for content analysis purposes, the German abstracts will be more difficult to analyze than their English counterpart. A brief analysis confirms that many of the missing German words, which do not therefore produce concept numbers assignable to the documents, are indeed important for content identification. Fig. 7, listing the words not found for document 0059 shows that 12 out of 14 missing words appear to be important for the analysis of that document. It would therefore seem essential that a more complete thesaurus be used under operational conditions and for future experiments. The other two collection characteristics, including the type of ~ 1~ ~; ~ 1~ ~1~ X ~ ,1~, ,l~,~ ,~. ~ accuracy of the relevance judgments are more difficult to assess, since these are not subject to statistical analysis. It is a fact that for some of the German documents informative abstracts are not available. For example, the abstract for document 028, included in Fig. 8, indicates that the corresponding document is a conference proceedings; very little is known about the subject matter of the conference, but the document was nevertheless judged relevant to six different queries (nos. 17, 27, 31, 32, 52, and 531 dealing with subjects as diverse as \"behavioral studies of information system users\" (query 17~, and \"the study of machine translation\" (query 27). One might quarrel with such relevance assessments, and with the inclusion of such documents in a test collection, particularly also since Fig. 6 (b} shows that the German queries operate more effectively with the English collection (using English relevance assessments) than with the German assessments. However, earlier studies using a variety of relevance assessments with the sam~document collection have shown that recallprecision results are not affected by ordinary differences in relevance assessments. [81 For this reason, it would be premature to assume that the performance differences are primarily due to distinctions in the relevance assessments or in the collection make-up.7. ConclusionAn experiment using a multi-lingual thesaurus in conjunction with two d~.fferent document collections, in German and English respectively, hasshown that cross-language processing (for example, German queries against English documents) is nearly as effective as processing within a single language. Furthermore, a simple translation of thesaurus categories appears '0 ,,J t~l-. ,.J. ,~' ~i, ~,~ .iI Uj uiI -", |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "TABREF1": { |
| "html": null, |
| "text": "the fact that relatively simple content analysis methods are generally preferable in a retrieval environment to more sophisticated methods.The foreign language processing to be described in the remainder of this study must be viewed in the light of the foregoing test results.", |
| "type_str": "table", |
| "content": "<table><tr><td colspan=\"2\">E~</td><td>m</td><td/><td/></tr><tr><td>'::3</td><td/><td/><td/><td/></tr><tr><td colspan=\"2\">\u00a2.3</td><td/><td/><td/></tr><tr><td>O r~</td><td/><td/><td/><td>0</td></tr><tr><td colspan=\"3\">4. Multi-lii~ual Thesaurus</td><td/><td/></tr><tr><td colspan=\"6\">The multi-lingual text processing experiment is motivated by the \u00a2.. \u00a2/] I-Precision 0~0 C C 13-.... D following principal considerations: W 0</td></tr><tr><td/><td/><td/><td/><td>(1)</td><td>.960 : .938</td></tr><tr><td/><td/><td>W</td><td/><td/><td>0.3</td><td>.834 I .776</td></tr><tr><td/><td/><td/><td/><td/><td>0.5</td><td>.769 : .735</td></tr><tr><td/><td/><td/><td/><td/><td>0.7</td><td>.706 I .625</td></tr><tr><td/><td/><td/><td/><td>\"o</td><td>0.9</td><td>.546 I .467 I</td></tr><tr><td>i .2</td><td>I .4</td><td>, .6</td><td>I .8</td><td>= -Recall 1.0 v -\u00ae I~.-</td></tr><tr><td colspan=\"6\">Comparison Between Statistical and Syntactic Phrases</td></tr><tr><td/><td/><td/><td colspan=\"2\">(averages aver 17 queries]</td></tr><tr><td/><td/><td/><td/><td>F\u00a3g, i</td></tr><tr><td/><td/><td/><td/><td/><td>with-</td></tr><tr><td colspan=\"2\">drawn from the file.</td><td colspan=\"4\">In order to insure that mixed language input is pro-</td></tr><tr><td colspan=\"6\">perly processed, the thesaurus must assign the same concept oategories~ no</td></tr><tr><td colspan=\"4\">matter what the input language.</td><td colspan=\"2\">The SMART system therefore utilizes a</td></tr></table>", |
| "num": null |
| }, |
| "TABREF3": { |
| "html": null, |
| "text": "iN WHAI WAYS ARE CDMPUIER SYSIEMS BELNG APPLIED IO RESEARCH iN THE FIELD OF IHE BELLES LEIIRES ? HAS MACHINE ANALYSIS OF LANGUAGE PROVED u~EFUL FOR INSIANC\u00a3, iN", |
| "type_str": "table", |
| "content": "<table><tr><td colspan=\"2\">DEIER~IJ~ING PKOBABLE AUTHORSHIP OF</td></tr><tr><td colspan=\"2\">ANONYMOUS ~ORKS OR i~ CQM@ILZNG</td></tr><tr><td>C ONC OdDANC E.S ?</td><td/></tr><tr><td colspan=\"2\">L)A~S WUEL SEN3 LES GALCULAIEUKS</td></tr><tr><td colspan=\"2\">;>UNI--IL3 APPLIQUEb A LA RECAHE~ttE UAN~</td></tr><tr><td colspan=\"2\">LE bOMAINE DES BE&LE$-LETIRE$ ? E$I-{,E</td></tr><tr><td colspan=\"2\">~UE L*ANALY.~t..,AUTOMAIIQUE DES IE&TE~ A</td></tr><tr><td colspan=\"2\">ETE UTL~.E, PAR ExEMPLE, POUR DETEKMANER</td></tr><tr><td colspan=\"2\">L\u00b0AUTEUR PROBABLE DoOUVKAGE~ ANUNVME~ UU</td></tr><tr><td colspan=\"2\">POUR, FA~RE DES C,~]N~UI~UAN~,E$ ?</td></tr><tr><td colspan=\"2\">INWIEwEIT HERUEN COMPUTER-SYSTEME ZUR</td></tr><tr><td>FOK~CHUN~ AUF UEM ~|ET</td><td>DER $CHUENEN</td></tr><tr><td colspan=\"2\">L|TEKAIUR VERWENDET ? HAT SIGH</td></tr><tr><td colspan=\"2\">MA~CH|NELLE SPRACHENANALYSE ALS</td></tr><tr><td colspan=\"2\">HILFRblCH ERH|E~EN, UM Z.~. DIE</td></tr><tr><td colspan=\"2\">VERMU|LIGHE AUIORENSGHAFT ~EI ANONYMEN</td></tr><tr><td colspan=\"2\">WERKEN ZU EEST|MMEN ODER UM.KONKORDANZEN</td></tr><tr><td>ZU&AMMENZUSIELLEN ?.</td><td/></tr></table>", |
| "num": null |
| } |
| } |
| } |
| } |
