Representação de conteúdo via indexação automática em textos integrais em língua portuguesa

Verifica-se a possibilidade da indexação automática derivativa de textos em língua portuguesa, a partir de seu texto integral. É aplicada a Fórmula de Transição de Goffman a 10 artigos na área de Bibliometria e formulado um algorltimo probabilístico de indexação. A Fórmula de Transição de Goffman ê perfeitamente aplicável à língua portuguesa, apontando para uma região de frequência de palavras onde estão concentradas as palavras indicativas do conteúdo dos artigos analisados. Palavras-chave Recuperação da informação. Indexação automática derivativa. Fórmula de Transição de Goffman. Representation of contents by the automatic indexing process of full texts in Portuguese language Abstract Possibility of automatic derived indexing of full texts in Portuguese is verifyed. Ten papers in Bibliometrics were indexed and their different parts considered for quantitative and qualitative analysis. Structure and disíríbution patterns of words were studied. Goffman's transition formula proved to be adequate as a slarting point for the indexing algorithm, which yielded, in all papers, a concentration zone forsemantic loaded terms. The algorithm worked as an uncenainty reducer, feading to the semantically important words. Keywords Information retrieval. Automatic derived indexing. Goffman's transition formula

Indexação automática e manual: revisão de literatura

Abordam-se as diversas pesquisas nacionais e estrangeiras que avaliam a qualidade da indexação manual e automática, em relação às técnicas e fontes empregadas para a extração dos termos significativos e a capacidade de recuperação da linguagem de indexação, nas bases de dados. Automatic and manual indexing: a literature review

A Hybrid Model for Document Retrieval Systems.

A methodology for the design of document retrieval systems is presented. First, a composite index term weighting model is developed based on term frequency statistics, including document frequency, relative frequency within document and relative frequency within collection, which can be adjusted by selecting various coefficients to fit into different indexing environments. Then, a composite retrieval model is proposed to process a user\u27s information request in a weighted Phrase-Oriented Fixed-Level Expression (POFLE), which may apply more than Boolean operators, through two phases. That is, we have a search for documents which are topically relevant to the information request by means of a descriptor matching mechanism, which incorporate a partial matching facility based on a structurally-restricted relationship imposed by indexing model, and is more general than matching functions of the traditional Boolean model and vector space model, and then we have a ranking of these topically relevant documents, by means of two types of heuristic-based selection rules and a knowledge-based evaluation function, in descending order of a preference score which predicts the combined effect of user preference for quality, recency, fitness and reachability of documents

Information Retrieval Performance Enhancement Using The Average Standard Estimator And The Multi-criteria Decision Weighted Set

Information retrieval is much more challenging than traditional small document collection retrieval. The main difference is the importance of correlations between related concepts in complex data structures. These structures have been studied by several information retrieval systems. This research began by performing a comprehensive review and comparison of several techniques of matrix dimensionality estimation and their respective effects on enhancing retrieval performance using singular value decomposition and latent semantic analysis. Two novel techniques have been introduced in this research to enhance intrinsic dimensionality estimation, the Multi-criteria Decision Weighted model to estimate matrix intrinsic dimensionality for large document collections and the Average Standard Estimator (ASE) for estimating data intrinsic dimensionality based on the singular value decomposition (SVD). ASE estimates the level of significance for singular values resulting from the singular value decomposition. ASE assumes that those variables with deep relations have sufficient correlation and that only those relationships with high singular values are significant and should be maintained. Experimental results over all possible dimensions indicated that ASE improved matrix intrinsic dimensionality estimation by including the effect of both singular values magnitude of decrease and random noise distracters. Analysis based on selected performance measures indicates that for each document collection there is a region of lower dimensionalities associated with improved retrieval performance. However, there was clear disagreement between the various performance measures on the model associated with best performance. The introduction of the multi-weighted model and Analytical Hierarchy Processing (AHP) analysis helped in ranking dimensionality estimation techniques and facilitates satisfying overall model goals by leveraging contradicting constrains and satisfying information retrieval priorities. ASE provided the best estimate for MEDLINE intrinsic dimensionality among all other dimensionality estimation techniques, and further, ASE improved precision and relative relevance by 10.2% and 7.4% respectively. AHP analysis indicates that ASE and the weighted model ranked the best among other methods with 30.3% and 20.3% in satisfying overall model goals in MEDLINE and 22.6% and 25.1% for CRANFIELD. The weighted model improved MEDLINE relative relevance by 4.4%, while the scree plot, weighted model, and ASE provided better estimation of data intrinsic dimensionality for CRANFIELD collection than Kaiser-Guttman and Percentage of variance. ASE dimensionality estimation technique provided a better estimation of CISI intrinsic dimensionality than all other tested methods since all methods except ASE tend to underestimate CISI document collection intrinsic dimensionality. ASE improved CISI average relative relevance and average search length by 28.4% and 22.0% respectively. This research provided evidence supporting a system using a weighted multi-criteria performance evaluation technique resulting in better overall performance than a single criteria ranking model. Thus, the weighted multi-criteria model with dimensionality reduction provides a more efficient implementation for information retrieval than using a full rank model

Language Models and Smoothing Methods for Information Retrieval

Language Models and Smoothing Methods for Information Retrieval (Sprachmodelle und Glättungsmethoden für Information Retrieval) Najeeb A. Abdulmutalib Kurzfassung der Dissertation Retrievalmodelle bilden die theoretische Grundlage für effektive Information-Retrieval-Methoden. Statistische Sprachmodelle stellen eine neue Art von Retrievalmodellen dar, die seit etwa zehn Jahren in der Forschung betrachtet werde. Im Unterschied zu anderen Modellen können sie leichter an spezifische Aufgabenstellungen angepasst werden und liefern häufig bessere Retrievalergebnisse. In dieser Dissertation wird zunächst ein neues statistisches Sprachmodell vorgestellt, das explizit Dokumentlängen berücksichtigt. Aufgrund der spärlichen Beobachtungsdaten spielen Glättungsmethoden bei Sprachmodellen eine wichtige Rolle. Auch hierfür stellen wir eine neue Methode namens 'exponentieller Glättung' vor. Der experimentelle Vergleich mit konkurrierenden Ansätzen zeigt, dass unsere neuen Methoden insbesondere bei Kollektionen mit stark variierenden Dokumentlängen überlegene Ergebnisse liefert. In einem zweiten Schritt erweitern wir unseren Ansatz auf XML-Retrieval, wo hierarchisch strukturierte Dokumente betrachtet werden und beim fokussierten Retrieval möglichst kleine Dokumentteile gefunden werden sollen, die die Anfrage vollständig beantworten. Auch hier demonstriert der experimentelle Vergleich mit anderen Ansätzen die Qualität unserer neu entwickelten Methoden. Der dritte Teil der Arbeit beschäftigt sich mit dem Vergleich von Sprachmodellen und der klassischen tf*idf-Gewichtung. Neben einem besseren Verständnis für die existierenden Glättungsmethoden führt uns dieser Ansatz zur Entwicklung des Verfahrens der 'empirischen Glättung'. Die damit durchgeführten Retrievalerexperimente zeigen Verbesserungen gegenüber anderen Glättungsverfahren.Language Models and Smoothing Methods for Information Retrieval Najeeb A. Abdulmutalib Abstract of the Dissertation Designing an effective retrieval model that can rank documents accurately for a given query has been a central problem in information retrieval for several decades. An optimal retrieval model that is both effective and efficient and that can learn from feedback information over time is needed. Language models are new generation of retrieval models and have been applied since the last ten years to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, they can be more easily adapted to model non traditional and complex retrieval problems and empirically they tend to achieve comparable or better performance than the traditional models. Developing new language models is currently an active research area in information retrieval. In the first stage of this thesis we present a new language model based on an odds formula, which explicitly incorporates document length as a parameter. To address the problem of data sparsity where there is rarely enough data to accurately estimate the parameters of a language model, smoothing gives a way to combine less specific, more accurate information with more specific, but noisier data. We introduce a new smoothing method called exponential smoothing, which can be combined with most language models. We present experimental results for various language models and smoothing methods on a collection with large document length variation, and show that our new methods compare favourably with the best approaches known so far. We discuss the collection effect on the retrieval function, where we investigate the performance of well known models and compare the results conducted using two variant collections. In the second stage we extend the current model from flat text retrieval to XML retrieval since there is a need for content-oriented XML retrieval systems that can efficiently and effectively store, search and retrieve information from XML document collections. Compared to traditional information retrieval, where whole documents are usually indexed and retrieved as single complete units, information retrieval from XML documents creates additional retrieval challenges. By exploiting the logical document structure, XML allows for more focussed retrieval that identifies elements rather than documents as answers to user queries. Finally we show how smoothing plays a role very similar to that of the idf function: beside the obvious role of smoothing, it also improves the accuracy of the estimated language model. The within document frequency and the collection frequency of a term actually influence the probability of relevance, which led us to a new class of smoothing function based on numeric prediction, which we call empirical smoothing. Its retrieval quality outperforms that of other smoothing methods

IfD - information for discrimination

The problem of term mismatch and ambiguity has long been serious and outstanding in IR. The problem can result in the system formulating an incomplete and imprecise query representation, leading to a failure of retrieval. Many query reformulation methods have been proposed to address the problem. These methods employ term classes which are considered as related to individual query terms. They are hindered by the computational cost of term classification, and by the fact that the terms in some class are generally related to some specific query term belonging to the class rather than relevant to the context of the query. In this thesis we propose a series of methods for automatic query reformulation (AQR). The methods constitute a formal model called IfD, standing for Information for Discrimination. In IfD, each discrimination measure is modelled as information contained in terms supporting one of two opposite hypotheses. The extent of association of terms with the query can thus be defined based directly on the discrimination. The strength of association of candidate terms with the query can then be computed, and good terms can be selected to enhance the query. Justifications for IfD are presented from several aspects: formal interpretations of infor­mation for discrimination are introduced to show its soundness; criteria are put forward to show its rationality; properties of discrimination measures are analysed to show its appro­priateness; examples are examined to show its usability; extension is discussed to show its potential; implementation is described to show its feasibility; comparisons with other methods are made to show its flexibility; improvements in retrieval performance are exhibited to show its powerful capability. Our conclusion is that the advantage and promise IfD should make it an indispensable methodology for AQR, which we believe can be an effective technique for improvement in retrieval performance

Interactive query expansion and relevance feedback for document retrieval systems

This thesis is aimed at investigating interactive query expansion within the context of a relevance feedback system that uses term weighting and ranking in searching online databases that are available through online vendors. Previous evaluations of relevance feedback systems have been made in laboratory conditions and not in a real operational environment. The research presented in this thesis followed the idea of testing probabilistic retrieval techniques in an operational environment. The overall aim of this research was to investigate the process of interactive query expansion (IQE) from various points of view including effectiveness. The INSPEC database, on both Data-Star and ESA-IRS, was searched online using CIRT, a front-end system that allows probabilistic term weighting, ranking and relevance feedback. The thesis is divided into three parts. Part I of the thesis covers background information and appropriate literature reviews with special emphasis on the relevance weighting theory (Binary Independence Model), the approaches to automatic and semi-automatic query expansion, the ZOOM facility of ESA/IRS and the CIRT front-end. Part II is comprised of three Pilot case studies. It introduces the idea of interactive query expansion and places it within the context of the weighted environment of CIRT. Each Pilot study looked at different aspects of the query expansion process by using a front-end. The Pilot studies were used to answer methodological questions and also research questions about the query expansion terms. The knowledge and experience that was gained from the Pilots was then applied to the methodology of the study proper (Part III). Part III discusses the Experiment and the evaluation of the six ranking algorithms. The Experiment was conducted under real operational conditions using a real system, real requests, and real interaction. Emphasis was placed on the characteristics of the interaction, especially on the selection of terms for query expansion. Data were collected from 25 searches. The data collection mechanisms included questionnaires, transaction logs, and relevance evaluations. The results of the Experiment are presented according to their treatment of query expansion as main results and other findings in Chapter 10. The main results discuss issues that relate directly to query expansion, retrieval effectiveness, the correspondence of the online-to-offline relevance judgements, and the performance of the w(p — q) ranking algorithm. Finally, a comparative evaluation of six ranking algorithms was performed. The yardstick for the evaluation was provided by the user relevance judgements on the lists of the candidate terms for query expansion. The evaluation focused on whether there are any similarities in the performance of the algorithms and how those algorithms with similar performance treat terms. This abstract refers only to the main conclusions drawn from the results of the Experiment: (1) One third of the terms presented in the list of candidate terms was on average identified by the users as potentially useful for query expansion; (2) These terms were mainly judged as either variant expression (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 relationship of the 5 best terms chosen by the users for query expansion to the initial query terms was: (a) 34% have no relationship or other type of correspondence with a query term; (b) 66% of the query expansion terms have a relationship which makes the term: (bl) narrower term (70%), (b2) broader term (5%), (b3) related term (25%). (4) The results provide some evidence for the effectiveness of interactive query expansion. The initial search produced on average 3 highly relevant documents at a precision of 34%; the query expansion search produced on average 9 further highly relevant documents at slightly higher precision. (5) The results demonstrated the effectiveness of the w(p—q) algorithm, for the ranking of terms for query expansion, within the context of the Experiment. (6) The main results of the comparative evaluation of the six ranking algorithms, i.e. w(p — q), EMIM, F4, F4modifed, Porter and ZOOM, are that: (a) w(p — q) and EMIM performed best; and (b) the performance between w(p — q) and EMIM and between F4 and F4modified is very similar; (7) A new ranking algorithm is proposed as the result of the evaluation of the six algorithms. Finally, an investigation is by definition an exploratory study which generates hypotheses for future research. Recommendations and proposals for future research are given. The conclusions highlight the need for more research on weighted systems in operational environments, for a comparative evaluation of automatic vs interactive query expansion, and for user studies in searching weighted systems

Knowledge Based Information Retrieval: A Semiotic Approach

The overall objective of this study is to analyze the document retrieval process and the main information retrieval (IR) concepts from the point of view of semiotics and design retrieval mechanisms based on the findings of the semiotic analysis of the retrieval situation. Semiotics is a discipline which studies 'sign systems' and how signs are exchanged in communication. The semiotic view of IR interaction presented in this dissertation views document retrieval as a kind of human communication process taking place in a social and cultural realm. The most important result of the semiotic model developed is the explication of the distinction between the knowledge production and transfer functions of document retrieval. The consequence of this finding is the conceptualization of the retrieval process as a dynamic and complex interplay between knowledge production and transfer tasks. It is hypothesised that, in the case of knowledge production, users of retrieval systems are interested in exploring new areas of the document collection which are not a priori known. Two knowledge based systems are developed based on the Okapi probabilistic retrieval system. The purpose of the retrieval systems designed is posited, in general terms, as to suggest the users new search areas of potential interest. This is achieved by treating the Inspec thesaurus as a semantic network, and applying a heuristic spreading activation technique to generate clusters of terms linked in the Inspec thesaurus. Each cluster or batch of terms is conceived as representing a part of the general search area defined by the initial user search terms. The main design objective here is to enable the user to identify new search areas from the term information contained in the batches. Two evaluation experiments were carried out with real users who had real information needs to test whether the batches were actually effective in defining search areas related to the original user queries and whether they were useful in pointing new areas which were potentially relevant to the users. A number of hypotheses related to the retrieval effectiveness of the knowledge based systems designed were also tested in the experiments. The main findings of the experiments indicate that: • the batches were useful in representing search domains relevant to the users' queries • in many cases the batches represented new ideas or new search domains to the users • the knowledge based systems had similar retrieval effectiveness in terms of precision as the Okapi syste