Examining and improving the effectiveness of relevance feedback for retrieval of scanned text documents

Important legacy paper documents are digitized and collected in online accessible archives. This enables the preservation, sharing, and significantly the searching of these documents. The text contents of these document images can be transcribed automatically using OCR systems and then stored in an information retrieval system. However, OCR systems make errors in character recognition which have previously been shown to impact on document retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of scanned document images. Our experimental examination of the effects of character recognition errors on an ad hoc OCR retrieval task demonstrates that, while baseline information retrieval can remain relatively unaffected by transcription errors, relevance feedback via query expansion becomes highly unstable. This paper examines the reason for this behaviour, and introduces novel modifications to standard relevance feedback methods. These methods are shown experimentally to improve the effectiveness of relevance feedback for errorful OCR transcriptions. The new methods combine similar recognised character strings based on term collection frequency and a string edit-distance measure. The techniques are domain independent and make no use of external resources such as dictionaries or training data

On-line learning for adaptive text filtering.

Yu Kwok Leung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 91-96).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- The Problem --- p.1Chapter 1.2 --- Information Filtering --- p.2Chapter 1.3 --- Contributions --- p.7Chapter 1.4 --- Organization Of The Thesis --- p.10Chapter 2 --- Related Work --- p.12Chapter 3 --- Adaptive Text Filtering --- p.22Chapter 3.1 --- Representation --- p.22Chapter 3.1.1 --- Textual Document --- p.23Chapter 3.1.2 --- Filtering Profile --- p.28Chapter 3.2 --- On-line Learning Algorithms For Adaptive Text Filtering --- p.29Chapter 3.2.1 --- The Sleeping Experts Algorithm --- p.29Chapter 3.2.2 --- The EG-based Algorithms --- p.32Chapter 4 --- The REPGER Algorithm --- p.37Chapter 4.1 --- A New Approach --- p.37Chapter 4.2 --- Relevance Prediction By RElevant feature Pool --- p.42Chapter 4.3 --- Retrieving Good Training Examples --- p.45Chapter 4.4 --- Learning Dissemination Threshold Dynamically --- p.49Chapter 5 --- The Threshold Learning Algorithm --- p.50Chapter 5.1 --- Learning Dissemination Threshold Dynamically --- p.50Chapter 5.2 --- Existing Threshold Learning Techniques --- p.51Chapter 5.3 --- A New Threshold Learning Algorithm --- p.53Chapter 6 --- Empirical Evaluations --- p.55Chapter 6.1 --- Experimental Methodology --- p.55Chapter 6.2 --- Experimental Settings --- p.59Chapter 6.3 --- Experimental Results --- p.62Chapter 7 --- Integrating With Feature Clustering --- p.76Chapter 7.1 --- Distributional Clustering Algorithm --- p.79Chapter 7.2 --- Integrating With Our REPGER Algorithm --- p.82Chapter 7.3 --- Empirical Evaluation --- p.84Chapter 8 --- Conclusions --- p.87Chapter 8.1 --- Summary --- p.87Chapter 8.2 --- Future Work --- p.88Bibliography --- p.91Chapter A --- Experimental Results On The AP Corpus --- p.97Chapter A.1 --- The EG Algorithm --- p.97Chapter A.2 --- The EG-C Algorithm --- p.98Chapter A.3 --- The REPGER Algorithm --- p.100Chapter B --- Experimental Results On The FBIS Corpus --- p.102Chapter B.1 --- The EG Algorithm --- p.102Chapter B.2 --- The EG-C Algorithm --- p.103Chapter B.3 --- The REPGER Algorithm --- p.105Chapter C --- Experimental Results On The WSJ Corpus --- p.107Chapter C.1 --- The EG Algorithm --- p.107Chapter C.2 --- The EG-C Algorithm --- p.108Chapter C.3 --- The REPGER Algorithm --- p.11

Concept-based Interactive Query Expansion Support Tool (CIQUEST)

This report describes a three-year project (2000-03) undertaken in the Information Studies Department at The University of Sheffield and funded by Resource, The Council for Museums, Archives and Libraries. The overall aim of the research was to provide user support for query formulation and reformulation in searching large-scale textual resources including those of the World Wide Web. More specifically the objectives were: to investigate and evaluate methods for the automatic generation and organisation of concepts derived from retrieved document sets, based on statistical methods for term weighting; and to conduct user-based evaluations on the understanding, presentation and retrieval effectiveness of concept structures in selecting candidate terms for interactive query expansion. The TREC test collection formed the basis for the seven evaluative experiments conducted in the course of the project. These formed four distinct phases in the project plan. In the first phase, a series of experiments was conducted to investigate further techniques for concept derivation and hierarchical organisation and structure. The second phase was concerned with user-based validation of the concept structures. Results of phases 1 and 2 informed on the design of the test system and the user interface was developed in phase 3. The final phase entailed a user-based summative evaluation of the CiQuest system. The main findings demonstrate that concept hierarchies can effectively be generated from sets of retrieved documents and displayed to searchers in a meaningful way. The approach provides the searcher with an overview of the contents of the retrieved documents, which in turn facilitates the viewing of documents and selection of the most relevant ones. Concept hierarchies are a good source of terms for query expansion and can improve precision. The extraction of descriptive phrases as an alternative source of terms was also effective. With respect to presentation, cascading menus were easy to browse for selecting terms and for viewing documents. In conclusion the project dissemination programme and future work are outlined

Inter-relaão das técnicas Term Extration e Query Expansion aplicadas na recuperação de documentos textuais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia e Gestão do ConhecimentoConforme Sighal (2006) as pessoas reconhecem a importância do armazenamento e busca da informação e, com o advento dos computadores, tornou-se possível o armazenamento de grandes quantidades dela em bases de dados. Em conseqüência, catalogar a informação destas bases tornou-se imprescindível. Nesse contexto, o campo da Recuperação da Informação, surgiu na década de 50, com a finalidade de promover a construção de ferramentas computacionais que permitissem aos usuários utilizar de maneira mais eficiente essas bases de dados. O principal objetivo da presente pesquisa é desenvolver um Modelo Computacional que possibilite a recuperação de documentos textuais ordenados pela similaridade semântica, baseado na intersecção das técnicas de Term Extration e Query Expansion

Noise-tolerance feasibility for restricted-domain Information Retrieval systems

Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented

Automatic indexing : an approach using an index term corpus and combining linguistic and statistical methods

This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with index terms. Index terms can be used as meta-information that describes documents, and that is used for seeking information. The main point of this thesis is to illustrate the process of developing an automatic indexer which analyses the content of documents by combining evidence from word frequencies and evidence from linguistic analysis provided by a syntactic parser. The indexer weights the expressions of a text according to their estimated importance for describing the content of a given document on the basis of the content analysis. The typical linguistic features of index terms were explored using a linguistically analysed text collection where the index terms are manually marked up. This text collection is referred to as an index term corpus. Specific features of the index terms provided the basis for a linguistic term-weighting scheme, which was then combined with a frequency-based term-weighting scheme. The use of an index term corpus like this as training material is a new method of developing an automatic indexer. The results of the experiments were promising

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org