-
McCune, B.P.; Tong, R.M.; Dean, J.S.: Rubric: a system for rule-based information retrieval (1985)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.440-445.
-
Schwarz, C.: Linguistische Hilfsmittel beim Information Retrieval (1984)
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-
Wenzel, F.: Semantische Eingrenzung im Freitext-Retrieval auf der Basis morphologischer Segmentierungen (1980)
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- Abstract
- The basic problem in freetext retrieval is that the retrieval language is not properly adapted to that of the author. Morphological segmentation, where words with the same root are grouped together in the inverted file, is a good eliminator of noise and information loss, providing high recall but low precision
-
Rau, L.F.; Jacobs, P.S.; Zernik, U.: Information extraction and text summarization using linguistic knowledge acquisition (1989)
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- Abstract
- Storing and accessing texts in a conceptual format has a number of advantages over traditional document retrieval methods. A conceptual format facilitates natural language access to text information. It can support imprecise and inexact queries, conceptual information summarisation, and, ultimately, document translation. Describes 2 methods which have been implemented in a prototype intelligent information retrieval system calles SCISOR (System for Conceptual Information Summarisation, Organization and Retrieval). Describes the text processing, language acquisition, and summarisation components of SCISOR
- Source
- Information processing and management. 25(1989) no.4, S.419-428
-
Metzler, D.P.; Haas, S.W.: ¬The constituent object parser : syntactic structure matching for information retrieval (1989)
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- Abstract
- The constituent object parser is designed to improve the precision and recall performance of information retrieval by providing more powerful matching procedures. Describes the dependency tree representations and the relationship between the intended use of the parser and its design.
- Source
- ACM transactions on information systems. 7(1989) no.3, S.292-316
-
Rau, L.F.: Conceptual information extraction and retrieval from natural language input (198)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.527-533
-
Schwarz, C.: Natural language and information retrieval : Kommentierte Literaturliste zu Systemen, Verfahren und Tools (1986)
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-
Informationslinguistische Texterschließung (1986)
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- LCSH
- Information storage and retrieval systems / Linguistics
- RSWK
- Information Retrieval / Aufsatzsammlung (DNB)
Linguistik / Information Retrieval / Aufsatzsammlung (SWB / BVB)
- Subject
- Information Retrieval / Aufsatzsammlung (DNB)
Linguistik / Information Retrieval / Aufsatzsammlung (SWB / BVB)
Information storage and retrieval systems / Linguistics
-
Porter, M.F.: ¬An algorithm for suffix stripping (1980)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.313-316.
-
Metzler, D.P.; Haas, S.W.; Cosic, C.L.; Wheeler, L.H.: Constituent object parsing for information retrieval and similar text processing problems (1989)
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- Abstract
- Describes the architecture and functioning of the Constituent Object Parser. This system has been developed specially for text processing applications such as information retrieval, which can benefit from structural comparisons between elements of text such as a query and a potentially relevant abstract. Describes the general way in which this objective influenced the design of the system.
- Source
- Journal of the American Society for Information Science. 40(1989) no.6, S.398-423
-
Hayes, P.J.; Knecht, L.E.; Cellio, M.J.: ¬A news story categorization system (1988)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.518-526
-
Salton, G.: Automatic processing of foreign language documents (1985)
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- Abstract
- The attempt to computerize a process, such as indexing, abstracting, classifying, or retrieving information, begins with an analysis of the process into its intellectual and nonintellectual components. That part of the process which is amenable to computerization is mechanical or algorithmic. What is not is intellectual or creative and requires human intervention. Gerard Salton has been an innovator, experimenter, and promoter in the area of mechanized information systems since the early 1960s. He has been particularly ingenious at analyzing the process of information retrieval into its algorithmic components. He received a doctorate in applied mathematics from Harvard University before moving to the computer science department at Cornell, where he developed a prototype automatic retrieval system called SMART. Working with this system he and his students contributed for over a decade to our theoretical understanding of the retrieval process. On a more practical level, they have contributed design criteria for operating retrieval systems. The following selection presents one of the early descriptions of the SMART system; it is valuable as it shows the direction automatic retrieval methods were to take beyond simple word-matching techniques. These include various word normalization techniques to improve recall, for instance, the separation of words into stems and affixes; the correlation and clustering, using statistical association measures, of related terms; and the identification, using a concept thesaurus, of synonymous, broader, narrower, and sibling terms. They include, as weIl, techniques, both linguistic and statistical, to deal with the thorny problem of how to automatically extract from texts index terms that consist of more than one word. They include weighting techniques and various documentrequest matching algorithms. Significant among the latter are those which produce a retrieval output of citations ranked in relevante order. During the 1970s, Salton and his students went an to further refine these various techniques, particularly the weighting and statistical association measures. Many of their early innovations seem commonplace today. Some of their later techniques are still ahead of their time and await technological developments for implementation. The particular focus of the selection that follows is an the evaluation of a particular component of the SMART system, a multilingual thesaurus. By mapping English language expressions and their German equivalents to a common concept number, the thesaurus permitted the automatic processing of German language documents against English language queries and vice versa. The results of the evaluation, as it turned out, were somewhat inconclusive. However, this SMART experiment suggested in a bold and optimistic way how one might proceed to answer such complex questions as What is meant by retrieval language compatability? How it is to be achieved, and how evaluated?
- Footnote
- Original in: Journal of the American Society for Information Science 21(1970) no.3, S.187-194.
-
Fagan, J.L.: ¬The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrieval (1989)
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- Abstract
- It may be possible to improve the quality of automatic indexing systems by using complex descriptors, for example, phrases, in addition to the simple descriptors (words or word stems) that are normally used in automatically constructed representations of document content. This study is directed toward the goal of developing effective methods of identifying phrases in natural language text from which good quality phrase descriptors can be constructed. The effectiveness of one method, a simple nonsyntactic phrase indexing procedure, has been tested on five experimental document collections. The results have been analyzed in order to identify the inadequacies of the procedure, and to determine what kinds of information about text structure are needed in order to construct phrase descriptors that are good indicators of document content. Two primary conclusions have been reached: (1) In the retrieval experiments, the nonsyntactic phrase construction procedure did not consistently yield substantial improvements in effectiveness. It is therefore not likely that phrase indexing of this kind will prove to be an important method of enhancing the performance of automatic document indexing and retrieval systems in operational environments. (2) Many of the shortcomings of the nonsyntactic approach can be overcome by incorporating syntactic information into the phrase construction process. However, a general syntactic analysis facility may be required, since many useful sources of phrases cannot be exploited if only a limited inventory of syntactic patterns can be recognized. Further research should be conducted into methods of incorporating automatic syntactic analysis into content analysis for document retrieval.
- Source
- Journal of the American Society for Information Science. 40(1989) no.2, S.115-132
-
Hahn, U.: Informationslinguistik : I: Einführung in das linguistische Information Retrieval (1985)
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- Abstract
- (1) "Informationslinguistik I: Einfuehrung in das linguistische Information Retrieval" (2) "Informationslinguistik II: linguistische und statistische Verfahren im experimentellen Information Retrieval" (3) "Intelligente Informationssysteme: Verfahren der Kuenstlichen Intelligenz im experimentellen Information Retrieval" Kursabschnitt zu natuerlichsprachlichen Systemen (4) Spezialkurse zum automatischen Uebersetzen, Indexing und Retrieval, Abstracting usf. dienen zur Vertiefung informationslinguistischer Spezialthemen Die Kurse (1) und (3) gehoeren zu dem Pool der Pflichtveranstaltungen aller Studenten des Diplom-Studiengangs Informationswissenschaft, waehrend (2) und (4) lediglich zu den Wahlpflichtveranstaltungen zaehlen, die aber obligatorisch fuer die Studenten des Diplomstudiengangs sind, die ihren Schwerpunkt (z.B. in Form der Diplomarbeit) im Bereich Informationslinguistik suchen - fuer alle anderen Studenten zaehlen diese Kurse zum Zusatz angebot an Lehrveranstaltungen.
- Content
- 2. Teil u.d.T.: Linguistische und statistische Verfahren im experimentellen Information Retrieval
-
Warner, A.J.: Natural language processing (1987)
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- Source
- Annual review of information science and technology. 22(1987), S.79-108
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Hahn, U.: Informationslinguistik : II: Einführung in das linguistische Information Retrieval (1985)
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- Abstract
- (1) "Informationslinguistik I: Einfuehrung in das linguistische Information Retrieval" (2) "Informationslinguistik II: linguistische und statistische Verfahren im experimentellen Information Retrieval" (3) "Intelligente Informationssysteme: Verfahren der Kuenstlichen Intelligenz im experimentellen Information Retrieval" Kursabschnitt zu natuerlichsprachlichen Systemen (4) Spezialkurse zum automatischen Uebersetzen, Indexing und Retrieval, Abstracting usf. dienen zur Vertiefung informationslinguistischer Spezialthemen Die Kurse (1) und (3) gehoeren zu dem Pool der Pflichtveranstaltungen aller Studenten des Diplom-Studiengangs Informationswissenschaft, waehrend (2) und (4) lediglich zu den Wahlpflichtveranstaltungen zaehlen, die aber obligatorisch fuer die Studenten des Diplomstudiengangs sind, die ihren Schwerpunkt (z.B. in Form der Diplomarbeit) im Bereich Informationslinguistik suchen - fuer alle anderen Studenten zaehlen diese Kurse zum Zusatz angebot an Lehrveranstaltungen.
Das vorliegende Skript entspricht dem Inhalt des Kurses "Informationslinguistik II" im SS 1983 bzw. SS 1984. Es ist im Juli 1983 inhaltlich abgeschlossen und im Januar 1985 lediglich redaktionell ueberarbeitet worden. Die Erstellung des Skripts entspricht einem dezidierten Auftrag des Projekts "Informationsvermittlung", der die Entwicklung geeigneter Lehrmaterialien zum informationswissenschaftlichen Aufbaustudium vorsah. Aufgrund des engen Projektzeitrahmens (1982-84) kann das Skript nicht in dem Masse voll ausgereift und ausformuliert sein, wie es gaengigen Standards entspraeche. Im Unterschied zum Skript "Informationslinguistik I" (HAHN 1985) laesst das vorliegende Skript wahlweise eine eher methoden- oder mehr systembezogene Darstellung informationslinguistischer Konzepte des experimentellen Information Retrieval zu (beides zusammen schliesst der enge Zeitrahmen eines Sommersemesters ausl). Die Entscheidung darueber sollte wenn moeglich in Abhaengigkeit zur personellen Zusammensetzung des Kurses getroffen werden, wobei - sofern die bislang genachten Erfahrungen verallgemeinerbar sind - sich bei einem nicht ausschliesslich an einer informationslinguistischen Spezialisierung interessierten und damit heterogenen Publikum die mehr systembezogene Praesentation als fuer das Verstaendnis informationslinguistischer Fragestellungen und entsprechender Verfahrensloesungen guenstiger gezeigt hat. Innerhalb dieser Nuancierung besitzt aber auch dieses Skript schon eine akzeptable inhaltliche Stabilitaet. Nichtsdestotrotz sollte gerade die Veroeffentlichung des Skripts als Anregung dienen, kritische Kommentare, Anmerkungen und Ergaenzungen zu diesem curricularen Entwurf herauszufordern, um damit die weitere disziplinaere Klaerung der Informationslinguistik zu foerdern.
- Content
- 1. Teil u.d.T.: Einführung in das linguistische Information Retrieval
-
Asija, S.P.: Natural language interface without artifical intelligence (1989)
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- Abstract
- SWIFT-ANSWER (Special Word Indexed Full Text Alpha Numeric Storage With Easy Retrieval) is a natural language interface that allows searchers to communicate with the computer in their own languages. The system operates without the need for artificial intelligence.
- Imprint
- Medford, NJ : Learned Information
-
Ruge, G.; Schwarz, C.: ¬Die Leistungsfähigkeit von linguistischen Verfahren in der Massentextverarbeitung (1989)
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- Abstract
- Dependenzstrukturen stellen syntagmatische Relationen von Worten in Texten dar. Ihre Anwendungsmöglichkeiten im Information Retrieval werden erläutert. Bei Siemens wurde ein System zur Transformation von Texten in Dependenzstrukturen entwickelt, wobei besonders darauf geachtet wurde, die Wirkung gegen den Aufwand abzuwiegen. Die letzte Version verarbeitet 20 MB Freitext in einer Stunde Realzeit auf einem Siemens BS2000 Großrechner. Analyse-Recall and Analyse-Precision liegen jeweils bei 0,85
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Schwarz, C.: THESYS: Thesaurus Syntax System : a fully automatic thesaurus building aid (1988)
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- Abstract
- THESYS is based on the natural language processing of free-text databases. It yields statistically evaluated correlations between words of the database. These correlations correspond to traditional thesaurus relations. The person who has to build a thesaurus is thus assisted by the proposals made by THESYS. THESYS is being tested on commercial databases under real world conditions. It is part of a text processing project at Siemens, called TINA (Text-Inhalts-Analyse). Software from TINA is actually being applied and evaluated by the US Department of Commerce for patent search and indexing (REALIST: REtrieval Aids by Linguistics and STatistics)
- Date
- 6. 1.1999 10:22:07
-
Needham, R.M.; Sparck Jones, K.: Keywords and clumps (1985)
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- Abstract
- The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.
