Search (7 results, page 1 of 1)

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Abstract

Many terminology engineering processes involve the task of automatic terminology extraction: before the terminology of a given domain can be modelled, organised or standardised, important concepts (or terms) of this domain have to be identified and fed into terminological databases. These serve in further steps as a starting point for compiling dictionaries, thesauri or maybe even terminological ontologies for the domain. For the extraction of the initial concepts, extraction methods are needed that operate on specialised language texts. On the other hand, many machine learning or information retrieval applications require automatic indexing techniques. In Machine Learning applications concerned with the automatic clustering or classification of texts, often feature vectors are needed that describe the contents of a given text briefly but meaningfully. These feature vectors typically consist of a fairly small set of index terms together with weights indicating their importance. Short but meaningful descriptions of document contents as provided by good index terms are also useful to humans: some knowledge management applications (e.g. topic maps) use them as a set of basic concepts (topics). The author believes that the tasks of terminology extraction and automatic indexing have much in common and can thus benefit from the same set of basic algorithms. It is the goal of this paper to outline some methods that may be used in both contexts, but also to find the discriminating factors between the two tasks that call for the variation of parameters or application of different techniques. The discussion of these methods will be based on statistical, syntactical and especially morphological properties of (index) terms. The paper is concluded by the presentation of some qualitative and quantitative results comparing statistical and morphological methods.

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Abstract

This paper presents a morphological classification of languages from the IR perspective. Linguistic typology research has shown that the morphological complexity of every language in the world can be described by two variables, index of synthesis and index of fusion. These variables provide a theoretical basis for IR research handling morphological issues. A common theoretical framework is needed in particular because of the increasing significance of cross-language retrieval research and CLIR systems processing different languages. The paper elaborates the linguistic morphological typology for the purposes of IR research. It studies how the indexes of synthesis and fusion could be used as practical tools in mono- and cross-lingual IR research. The need for semantic and syntactic typologies is discussed. The paper also reviews studies made in different languages on the effects of morphology and stemming in IR.

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Abstract

It is possible to locate over 60% of indexing terms used in the Current List of Medical Literature by analysing the titles of the articles. Citation indexes contain 'noise' and lack many pertinent citations. Mechanical indexing or analysis of text must begin with some linguistic technique. Discusses Harris' methods of structural linguistics, discourse analysis and transformational analysis. Provides 3 examples with references, abstracts and index entries

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Date

6. 3.1997 16:22:15

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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?

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Content

HARMAN, D.: Relevance feedback revisited; AALBERSBERG, I.J.: Incremental relevance feedback; TAGUE-SUTCLIFFE, J.: Measuring the informativeness of a retrieval process; LEWIS, D.D.: An evaluation of phrasal and clustered representations on a text categorization task; BLOSSEVILLE, M.J., G. HÉBRAIL, M.G. MONTEIL u. N. PÉNOT: Automatic document classification: natural language processing, statistical analysis, and expert system techniques used together; MASAND, B., G. LINOFF u. D. WALTZ: Classifying news stories using memory based reasoning; KEEN, E.M.: Term position ranking: some new test results; CROUCH, C.J. u. B. YANG: Experiments in automatic statistical thesaurus construction; GREFENSTETTE, G.: Use of syntactic context to produce term association lists for text retrieval; ANICK, P.G. u. R.A. FLYNN: Versioning of full-text information retrieval system; BURKOWSKI, F.J.: Retrieval activities in a database consisting of heterogeneous collections; DEERWESTER, S.C., K. WACLENA u. M. LaMAR: A textual object management system; NIE, J.-Y.:Towards a probabilistic modal logic for semantic-based information retrieval; WANG, A.W., S.K.M. WONG u. Y.Y. YAO: An analysis of vector space models based on computational geometry; BARTELL, B.T., G.W. COTTRELL u. R.K. BELEW: Latent semantic indexing is an optimal special case of multidimensional scaling; GLAVITSCH, U. u. P. SCHÄUBLE: A system for retrieving speech documents; MARGULIS, E.L.: N-Poisson document modelling; HESS, M.: An incrementally extensible document retrieval system based on linguistics and logical principles; COOPER, W.S., F.C. GEY u. D.P. DABNEY: Probabilistic retrieval based on staged logistic regression; FUHR, N.: Integration of probabilistic fact and text retrieval; CROFT, B., L.A. SMITH u. H. TURTLE: A loosely-coupled integration of a text retrieval system and an object-oriented database system; DUMAIS, S.T. u. J. NIELSEN: Automating the assignement of submitted manuscripts to reviewers; GOST, M.A. u. M. MASOTTI: Design of an OPAC database to permit different subject searching accesses; ROBERTSON, A.M. u. P. WILLETT: Searching for historical word forms in a database of 17th century English text using spelling correction methods; FAX, E.A., Q.F. CHEN u. L.S. HEATH: A faster algorithm for constructing minimal perfect hash functions; MOFFAT, A. u. J. ZOBEL: Parameterised compression for sparse bitmaps; GRANDI, F., P. TIBERIO u. P. Zezula: Frame-sliced patitioned parallel signature files; ALLEN, B.: Cognitive differences in end user searching of a CD-ROM index; SONNENWALD, D.H.: Developing a theory to guide the process of designing information retrieval systems; CUTTING, D.R., J.O. PEDERSEN, D. KARGER, u. J.W. TUKEY: Scatter/ Gather: a cluster-based approach to browsing large document collections; CHALMERS, M. u. P. CHITSON: Bead: Explorations in information visualization; WILLIAMSON, C. u. B. SHNEIDERMAN: The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploring system

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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.