Candidate Terms Research Articles

On bidirectional English–Arabic search

AbstractIn Cross‐Language Information Retrieval (CLIR), queries in one language retrieve relevant documents in other languages. Machine‐Readable Dictionaries (MRD) and Machine Translation (MT) systems are important resources for query translation in CLIR. We investigate the use of MT systems and MRD to Arabic–English and English–Arabic CLIR. The translation ambiguity associated with these resources is the key problem. We present three methods of query translation using a bilingual dictionary for Arabic–English CLIR. First, we present the Every‐Match (EM) method. This method yields ambiguous translations because many extraneous terms are added to the original query. To disambiguate query translation, we present the First‐Match (FM) method that considers the first match in the dictionary as the candidate term. Finally, we present the Two‐Phase (TP) method. We show that good retrieval effectiveness can be achieved without complex resources using the Two‐Phase method for Arabic–English CLIR. We also empirically evaluate the effectiveness of the Arabic–English MT approach using short, medium, and long queries of TREC7 and TREC9 topics and collections. The effects of the query length to the quality of the MT‐based CLIR are investigated. English–Arabic CLIR is evaluated via MRD and English–Arabic MT. The query expansion via posttranslation approach is used to deemphasize the extraneous terms introduced by the MRD and MT for English–Arabic CLIR.

Identifying diagnostic studies in MEDLINE: reducing the number needed to read.

The search filters in PubMed have become a cornerstone in information retrieval in evidence-based practice. However, the filter for diagnostic studies is not fully satisfactory, because sensitive searches have low precision. The objective of this study was to construct and validate better search strategies to identify diagnostic articles recorded on MEDLINE with special emphasis on precision. A comparative, retrospective analysis was conducted. Four medical journals were hand-searched for diagnostic studies published in 1989 and 1994. Four other journals were hand-searched for 1999. The three sets of studies identified were used as gold standards. A new search strategy was constructed and tested using the 1989-subset of studies and validated in both the 1994 and 1999 subsets. We identified candidate text words for search strategies using a word frequency analysis of the abstracts. According to the frequency of identified terms, searches were run for each term independently. The sensitivity, precision, and number needed to read (1/precision) of every candidate term were calculated. Terms with the highest sensitivity x precision product were used as free text terms in combination with the MeSH term "SENSITIVITY AND SPECIFICITY" using the Boolean operator OR. In the 1994 and 1999 subsets, we performed head-to-head comparisons of the currently available PubMed filter with the one we developed. The sensitivity, precision and the number needed to read (1/precision) were measured for different search filters. The most frequently occurring three truncated terms (diagnos*; predict* and accura*) in combination with the MeSH term "SENSITIVITY AND SPECIFICITY" produced a sensitivity of 98.1 percent (95% confidence interval: 89.9-99.9%) and a number needed to read of 8.3 (95% confidence interval: 6.7-11.3%). In direct comparisons of the new filter with the currently available one in PubMed using the 1994 and 1999 subsets, the new filter achieved better precision (12.0% versus 8.2% in 1994 and 5.0% versus 4.3% in 1999. The 95% confidence intervals for the differences range from 0.05% to 7.5% (p = 0.041) and -1.0% to 2.3% (p = 0.45), respectively). The new filter achieved slightly better sensitivities than the currently available one in both subsets, namely 98.1 and 96.1% (p = 0.32) versus 95.1 and 88.8% (p = 0.125). The quoted performance of the currently available filter for diagnostic studies in PubMed may be overstated. It appears that even single external validation may lead to over optimistic views of a filter's performance. Precision appears to be more unstable than sensitivity. In terms of sensitivity, our filter for diagnostic studies performed slightly better than the currently available one and it performed better with regards to precision in the 1994 subset. Additional research is required to determine whether these improvements are beneficial to searches in practice.

Exemplary documents: a foundation for information retrieval design

Documents are generally represented for retrieval by either extracting index terms from them or by creating and selecting from an external set of candidate terms. There are many procedures for doing this, but while work continues along these dimensions, there have been relatively few attempts to change this basic process. Of particular importance is the creation of indexing schemes for retrieval systems in non-library contexts. Here, the cost of developing an indexing scheme independent of the documents to be retrieved is often considered too high to implement. As a result, simple full-text retrieval or, to a lesser extent, automatic extractive or associative indexing methods are the predominant methods used in non-library contexts. This paper suggests an alternative document representation method based on what we call exemplary documents. Exemplary documents are those documents that describe or exhibit the intellectual structure of a particular field of interest. In so doing, they provide both an indexing vocabulary for that area and, more importantly, a narrative context in which the indexing terms have a clearer meaning. Further, it is much easier to develop an indexing scheme by using exemplary documents than it is to do so from scratch.

Disambiguation of single noun translations extracted from bilingual comparable corpora

Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.

TRUCKS: A Model for Automatic Multi-Word Term Recognition.

This paper examines the use of linguistic techniques in the area of automatic term recognition. It describes the TRUCKS model, which makes use of different types of contextual information-syntactic, semantic, terminological and statistical-seeking particularly to identify those parts of the context which are most relevant to terms. From an initial corpus of sublanguage texts, this identifies, disambiguates and ranks candidate terms. The system is evaluated with respect to the statistical approach on which it is built, and with respect to its expected theoretical performance. We show that by using deeper forms of contextual information, we can improve on the extraction of multi-word terms. The resulting list of ranked terms is shown to improve on that produced by traditional methods, in terms of precision and distribution, while the information acquired in the process can also be used for a variety of other applications, such as disambiguation, lexical tuning and term clustering.

MODELING NONLINEAR DETERMINISM IN SHORT TIME SERIES FROM NOISE DRIVEN DISCRETE AND CONTINUOUS SYSTEMS

Current methods for detecting deterministic chaos in a time series require long, stationary, and relatively noise-free data records. This limits the utility of these methods in most experimental and clinical settings. Recently we presented a new method for detecting determinism in a time series, and for assessing whether this determinism has chaotic attributes, i.e. sensitivity to initial conditions. The method is based on fitting a deterministic nonlinear autoregressive (NAR) model to the data [Chon et al., 1997]. This approach assumes that the noise in the model can be represented as a series of independent, identically distributed random variables. If this is not the case the accuracy of the algorithm may be compromised. To explicitly deal with the possibility of more complex noise structures, we present a method based on a stochastic NAR model. The method iteratively estimates NAR models for both the deterministic and the stochastic parts of the signal. An additional feature of the algorithm is that it includes only the significant autoregressive terms among the pool of candidate terms searched. As a result the algorithm results in a model with significantly fewer terms than a model obtained by traditional model order search criterions. Subsequently, Lyapunov exponents are calculated for the estimated models to examine if chaotic determinism (i.e. sensitivity to initial conditions) is present in the time series. The major advantages of this algorithm are: (1) it provides accurate parameter estimation with a small number of data points, (2) it is accurate for signal-to-noise ratios as low as -9 dB for discrete and -6 dB for continuous chaotic systems, and (3) it allows characterization of the dynamics of the system, and thus prediction of future states of the system, over short time scales. The stochastic NAR model is applied to renal tubular pressure data from normotensive and hypertensive rats. One form of hypertension was genetic, and the other was induced on normotensive rats by placing a restricting clip on one of their renal arteries. In both types of hypertensive rats, positive Lyapunov exponents were present, indicating that the fluctuations observed in the proximal tubular pressure were due to the operation of a system with chaotic determinism. In contrast, only negative exponents were found in the time series from normotensive rats.

Interactive query expansion: A user-based evaluation in a relevance feedback environment

A user-centered investigation of interactive query expansion within the context of a relevance feedback system is presented in this article. Data were collected from 25 searches using the INSPEC database. The data collection mechanisms included questionnaires, transaction logs, and relevance evaluations. The results discuss issues that relate to query expansion, retrieval effectiveness, the correspondence of the on-line-to-off-line relevance judgments, and the selection of terms for query expansion by users (interactive query expansion). The main conclusions drawn from the results of the study are that: (1) one-third of the terms presented to users in a list of candidate terms for query expansion was identified by the users as potentially useful for query expansion. (2) These terms were mainly judged as either variant expressions (synonyms) or alternative (related) terms to the initial query terms. However, a substantial portion of the selected terms were identified as representing new ideas. (3) The relationships identified between the five best terms selected by the users for query expansion and the initial query terms were that: (a) 34% of the query expansion terms have no relationship or other type of correspondence with a query term; (b) 66% of the remaining query expansion terms have a relationship to the query terms. These relationships were: narrower term (46%), broader term (3%), related term (17%). (4) The results provide evidence for the effectiveness of interactive query expansion. The initial search produced on average three highly relevant documents; the query expansion search produced on average nine further highly relevant documents. The conclusions highlight the need for more research on: interactive query expansion, the comparative evaluation of automatic vs. interactive query expansion, the study of weighted Web-based or Web-accessible retrieval systems in operational environments, and for user studies in searching ranked retrieval systems in general.

Structure detection for nonlinear rational models using genetic algorithms

A new nonlinear rational model identification algorithm is introduced based on genetic algorithms, Compared with other rational model identification approaches, the new algorithm has two main advantages. First, this algorithm does not require a linear-in-the-parameters regression equation and, as a consequence, the severe noise problems induced by multiplying out the rational model are avoided. Second, the new algorithm provides near-optimal global parameter estimation. Unfortunately, this is balanced by an enormous computational load even when identifying models which consist of modest parameter sets. Simulated examples are included to illustrate that the new algorithm works well on systems with modest candidate term sets but can fail when applied to systems with large candidate term sets.

Algorithms for minimal model structure detection in nonlinear dynamic system identification

The minimal model structure detection (MMSD ) problem in nonlinear dynamic system identification is formulated as a search for the optimal orthogonalization path. While an exhaustive search for a model with 20 candidate terms would involve 2.43 1018 possible paths, it is shown that this can typically be reduced to 2 103 by augmenting the orthogonal estimation algorithm with genetic search procedures. The MMSD algorithm provides the first practical solution for optimal structure detection in NARMAX modelling, training neural networks and fuzzy systems modelling. Based on the MMSD algorithm, a refined forward regression orthogonal (RFRO ) algorithm is developed. The RFRO algorithm initially detects a parsimonious model structure using the forward regression orthogonal algorithm and then refines the model structure by applying the MMSD algorithm to the reduced model term set. The RFRO algorithm cannot guarantee to find the minimal model structure, but it is computationally more efficient than the MMSD algorithm and can find a smaller model than the forward regression orthogonal algorithm.

Termight: Coordinating Humans and Machines in Bilingual Terminology Acquisition

We propose a semi-automatic tool, termight, that supports the construction of bilingual glossaries. Termight consists of two components which address the two subtasks in glossary construction: (a) preparing a monolingual list of all technical terms in a source-language document, and (b) finding the translations for these terms in parallel source–target documents. As a first step (in each component) the tool extracts automatically candidate terms and candidate translations, based on term-extraction and word-alignment algorithms. It then performs several additional preprocessing steps which greatly facilitate human post-editing of the candidate lists. These steps include grouping and sorting of candidates and associating example concordance lines with each candidate. Finally, the data prepared in preprocessing is presented to the user via an interactive interface which supports quick post-editing operations. Termight was deployed by translators at AT & T Business Translation Services (formerly AT & T Language Line Services) leading to very high rates of semi-automatic glossary construction.

Improved structure selection for nonlinear models based on term clustering

In this paper the concepts of term clusters and cluster coefficients are defined and used in the context of system identification. It is argued that if a certain type of term in a nonlinear model is spurious, the respective cluster coefficient is small compared with the coefficients of the other clusters represented in the model. Once the spurious clusters have been detected, the corresponding terms can be deleted from the set of candidate terms. The consequences of doing this are (i) a drastic reduction in the size of the set of candidate terms and, consequently, a substantial gain in computation time is achieved; (ii) the final estimated model is more likely to reproduce the dynamics of the original system; and (iii) the final model is more robust to overparametrization. Numerical examples are included to illustrate the new procedure.

Computer Applications in Comparative History: The Master & Servant Project at York University (Canada)

The Master and Servant Project at York University (Toronto) analyses employment laws torn a hundred Britishjurisdictionsoverfourcenturies, examiningthe lexical relationships among many hundreds of statutes, and seeking explanations for the clustering of particular legal provisions in particular parts of the imperial economy. The complexity, redundancy, and sheer bulk of the material invited computer analysis. Commercial database software (askSam) documents the paper archive; analysis reUes largely on programmes developed in the project and on TACT, a text retrievalpackage from the Centre for Computing in the Humanities, University of Toronto. Lexical content is approached with programmes that calculate the overlap oflanguage among sentences across the whole archive; high scores identify candidate terms for detailed examination in TACT. Analysis of the policy of the acts (independent of their lexical form) relies on coded versions of the statutes, created with a coding engine that minimizes errors. A conceptual clustering programme, in which the computer repeatedly sorts the coded versions according to subsets ofcharacteristics, groups the statutes into unambiguous sets with common features. The techniques described are applicable wherever the language or conceptual content oflarge numbers oftexts needs to be compared in detail.

AN EVALUATION OF INTERACTIVE QUERY EXPANSION IN AN ONLINE LIBRARY CATALOGUE WITH A GRAPHICAL USER INTERFACE

An online library catalogue served as a testbed to evaluate an interactive query expansion facility based on relevance feedback for the Okapi probabilistic term weighting retrieval system. The facility was implemented in a graphical user interface (gui) environment using a game‐board metaphor for the search process, and allowed searchers to select candidate terms extracted from relevant retrieved items to reformulate queries. The take‐up of the interactive query expansion option was found to be lower, and its retrieval performance less effective, compared to previous tests featuring automatic query expansion. Contributory factors including the number, presentation and source of terms are discussed.

Machine-aided indexing at NASA

This report describes the NASA Lexical Dictionary (NLD), a machine-aided indexing system used online at the National Aeronautics and Space Administration's Center for AeroSpace Information (CASI). This system automatically suggests a set of candidate terms from NASA's controlled vocabulary for any designated natural language text input. The system is comprised of a text processor that is based on the computational, nonsyntactic analysis of input text and an extensive knowledge base that serves to recognize and translate text-extracted concepts. The functions of the various NLD system components are described in detail, and production and quality benefits resulting from the implementation of machine-aided indexing at CASI are discussed.

Earmarking as a deterrent to rent-seeking

Statistical analysis tends to support the view that quid-pro-quo fiscal arrangements reduce rent-seeking opportunities and thereby limit campaign spending in gubernatorial races. This investigation has, however, not examined other possible avenues of rent-seeking, nor has it tested the hypothesis that earmarking forces a global reduction in rent-seeking activity. One possibility, already discussed, is that higher taxes may reduce participation in gubernatorial elections but increase the demand for tax advisers. Nor have we considered rent-seeking at different levels within government — within the legislature or the highway bureaucracy, for example. Finally, we have not examined rent-seeking at those critical quasi-constitutional moments when earmarking rules are being established. Because rent-seeking can occur on many different levels, these issues deserve further investigation. Of some interest is the statistical insignificance of several variables commonly thought to contribute to campaign activity, including the income of citizens, and the candidate's political PARTY and TERM of office. Our findings downplay the importance of those variables once the distinction is made between quid-pro-quo and unilateral government transactions. Buchanan's view that rent-seeking tends to increase in direct proportion to government spending gains support here, after controlling for earmarking and other conditions that influence campaign spending. Accordingly, a 1% increase in non-QPQ spending stimulates a 1% increase in campaign activity. The positive coefficient on GOVernment spending and the negative coefficient on TAXes imply that campaign contributions and SPENDing grow in conjunction with larger public deficits — an ominous sign in an age of contribution-maximizing candidates. More optimistically, our empirical results also suggest that earmarking and other quid-pro-quo fiscal arrangements may be among the institutional reforms most effective at limiting growth of the rent-seeking society.

Chern-Simons topological Lagrangians in odd dimensions and their Kaluza-Klein reduction

By clarifying the behavior of generic Chern-Simons secondary invariants under infinitesimal variation and finite gauge transformation, it is proved that they are eligible to be a candidate term in the Lagrangian in odd dimensions (2 k − 1 for gauge theories and 4 k − 1 for gravity). The coefficients in front of these terms may be quantized because of topological reasons. As a possible application, the dimensional reduction of such actions in Kaluza-Klein theory is discussed. The difficulty in defining the Chern-Simons action for topologically nontrivial field configurations is pointed out and resolved.

Consensus vs frequency: An empirical investigation of the theories for identifying descriptors in designing retrieval thesauri

The present-day guidlines for thesaurus design recommend the two different strategies—the committee and empirical approaches—for identifying candidate terms. An argument is made that the basis for the recommendation is the assumption that the knowledge based on the consensus of experts of a field is different from the knowledge expressed in the literature of that field. An experiment was conducted to test the validity of this assumption. The finding that the two strategies failed to generate the two significantly different lists of terms challenges the validity of the assumption and raises several important questions to the theorists who write the guidelines for thesaurus design and to those who must put the guidelines into practice for design of a thesaurus.