Significant Improvements In Retrieval Effectiveness Research Articles

CONTENT-BASED FEATURE FUSION REPRESENTATION FOR MARINE INVERTEBRATES

Marine species representation and retrieval is crucial for its studies and conservation. The images of these animals are usually captured underwater with complex background, at different angle, position, and size, which makes it very hard to provide a good representation with the current methods. Most of the current methods only support content-based representation for marine life images with clear background (taken in laboratory or in environments which have been set up), containing just one animal in an image, or the animal is positioned nicely at the centre of the image. Responding to these important needs, a multi-feature method for Content-based Image Retrieval (CBIR) that employs colour, shape, and texture information of marine life images is proposed. The colour feature vectors are obtained by extracting first and second order of Colour Moments. Shape information is constructed through the implementation of Discrete Wavelet transform up to four sub-bands and the extraction of Canny edge feature. Texture features are obtained with the Zernike Moments (ZM) of order four and the extraction of few Grey Level Co-occurrence Matrix properties. We conducted two experiments to determine the best order of ZM as well as to measure the retrieval performance of the proposed descriptor. Retrieval results based on marine invertebrate and Fish4Knowledge datasets clearly shown that the proposed method has effectively obtained the best precision value at 11 standard recall levels (72.42%) and MAP value (67.7%). The proposed method is further measured based on the statistical two-tailed paired t-test and has revealed a significant improvement in retrieval effectiveness.

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation.

Automatic query expansion: A structural linguistic perspective

A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co‐occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency‐based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus‐based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large‐scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.

A study of relevance feedback techniques in interactive multilingual information access

PurposeWith the vast amount of multilingual information available online, it becomes increasingly critical for libraries to use various multilingual information access techniques in order to effectively support patrons' online information requests. However, this is still a relatively under‐explored area. This paper aims to study the effectiveness and the adoptability of query expansion and translation enhancement in the context of interactive multilingual information access.Design/methodology/approachRelying on an interactive multilingual information access system called ICE‐TEA, the authors conducted a controlled experiment (English‐to‐Chinese translation) involving human subjects to assess the retrieval effectiveness, analyzed the collected search logs to examine users' behavior, and employed pre‐ and post‐questionnaires to obtain users' opinions about the system.FindingsThe results confirm that significant improvement in retrieval effectiveness can be achieved by combining query expansion with translation enhancement (as compared to a case when there is no relevance feedback). However, users' ability to understand, interact with and even perceive the complex process of searches involving the combination of query expansion and translation enhancement may greatly impact the effectiveness of the techniques. The results also confirm that human‐generated queries were short queries, which calls for careful consideration of how longer queries perform in real search because many search engines rely on longer and more complex queries.Originality/valueThis study examines two important relevance feedback techniques in the context of human‐involved multilingual information access. This study is a valuable addition to the information seeking behaviour literature.

Bayesian Inference Network Significantly Improves the Effectiveness of Similarity Searching Using Multiple 2D Fingerprints and Multiple Reference Structures

AbstractRecent work in similarity searching have suggested that significant improvements in retrieval effectiveness can be achieved by combining results from multiple reference structures or multiple molecular descriptors. Recently, the Bayesian inference network model (BIN) has been introduced for performing molecular similarity searching using a single and multiple reference structures. One of the important characteristics of the inference network model is that it permits the combination of multiple reference structures and multiple molecular descriptors. This paper introduces an inference network model developed for molecular similarity searching that integrates into a single framework, multiple reference structures and multiple molecular descriptors. The inference network model of similarity, which was designed from this point of view, treats similarity searching as an evidential reasoning process where multiple sources of evidence about reference and compound content are combined to estimate similarity scores. Our results show that, the BIN with multiple descriptors is notably more effective than BIN with a single descriptor in searches for structurally diverse sets of actives molecules.

Similarity‐Based Virtual Screening Using Bayesian Inference Network: Enhanced Search Using 2D Fingerprints and Multiple Reference Structures

AbstractIt has been known that different reference structure retrieve different sets of structures. Recent works in similarity searching have suggested that significant improvements in retrieval effectiveness can be achieved by combining results from different reference structures. One of an important characteristic of the Bayesian inference network (BIN) model is that permits the combining of multiple reference structures. In this paper we introduce a formal inference net model to directly combine the contributions of multiple reference structures, and propose a novel approach to the combination of information from various reference structures. The inference net model of similarity, which was designed from this point of view, treats similarity searching as an evidential reasoning process where multiple sources of evidence about target structure are combined to estimate similarity scores. In this paper, we have compared BIN with other similarity searching methods when multiple bioactive reference structures are available. Six different 2D fingerprints were used in combination with data fusion (DF) and nearest neighbor (NN) approaches as search tools and also as descriptors for BIN. Our empirical results show that the BIN consistently outperformed all conventional approaches such as DF and NN, regardless of the fingerprints that were tested. The superiority of BIN over conventional approaches is ascribed to the fact that BIN understands the content of the descriptors of the structures and references and used this understanding to infer the direct relationship between structures and references.

Error correction vs. query garbling for Arabic OCR document retrieval

Due to the existence of large numbers of legacy documents (such as old books and newspapers), improving retrieval effectiveness for OCR'ed documents continues to be an important problem. This article compares the effect of OCR error correction with and without language modeling and the effect of query garbling with weighted structured queries on the retrieval of OCR degraded Arabic documents. The results suggest that moderate error correction does not yield statistically significant improvement in retrieval effectiveness when indexing and searching using n-grams. Also, reversing error correction models to perform query garbling in conjunction with weighted structured queries yields improved retrieval effectiveness. Lastly, using very good error correction that utilizes language modeling yields the best improvement in retrieval effectiveness.

A novel dependency language model for information retrieval

This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) approach to IR by introducing dependency models for both query and document. Relevance between document and query is then evaluated by reference to the Kullback-Leibler divergence between their dependency models. This paper introduces a novel hybrid dependency structure, which allows integration of various forms of dependency within a single framework. A pseudo relevance feedback based method is also introduced for constructing query dependency model. The basic idea is to use query-relevant top-ranking sentences extracted from the top documents at retrieval time as the augmented representation of query, from which the relationships between query terms are identified. A Markov Random Field (MRF) based approach is presented to ensure the relevance of the extracted sentences, which utilizes the association features between query terms within a sentence to evaluate the relevance of each sentence. This dependency retrieval model was compared with other traditional retrieval models. Experiments indicated that it produces significant improvements in retrieval effectiveness.

데이터 결합이 웹 문서 검색성능에 미치는 영향 연구

This study investigates the effect of data fusion on the retrieval effectiveness by performing an experiment combining multiple representations of Web documents. The types of document representation combined in the study include content terms, links, anchor text, and URL. The experimental results showed that the data fusion technique combining document representation methods in Web environment did not bring any significant improvement in retrieval effectiveness.

LEARNING RETRIEVAL EXPERT COMBINATIONS WITH GENETIC ALGORITHMS

The goal of information retrieval (IR) is to provide models and systems that help users to identify the relevant documents to their information needs. Extensive research has been carried out to develop retrieval methods that solve this goal. These IR techniques range from purely syntax-based, considering only frequencies of words, to more semantics-aware approaches. However, it seems clear that there is no single method that works equally well on all collections and for all queries. Prior work suggests that combining the evidence from multiple retrieval experts can achieve significant improvements in retrieval effectiveness. A common problem of expert combination approaches is the selection of both the experts to be combined and the combination function. In most studies the experts are selected from a rather small set of candidates using some heuristics. Thus, only a reduced number of possible combinations is considered and other possibly better solutions are left out. In this paper we propose the use of genetic algorithms to find a suboptimal combination of experts for a document collection at hand. Our approach automatically determines both the experts to be combined and the parameters of the combination function. Because we learn this combination for each specific document collection, this approach allows us to automatically adjust the IR system to specific user needs. To learn retrieval strategies that generalize well on new queries we propose a fitness function that is based on the statistical significance of the average precision obtained on a set of training queries. We test and evaluate the approach on four classical text collections. The results show that the learned combination strategies perform better than any of the individual methods and that genetic algorithms provide a viable method to learn expert combinations. The experiments also evaluate the use of a semantic indexing approach, the context vector model, in combination with classical word matching techniques.

Document length normalization

In the TREC collection—a large full-text experimental text collection with widely varying document lengths—we observe that the likelihood of a document being judged relevant by a user increases with the document length. We show that a retrieval strategy, such as the vector-space cosine match, that retrieves documents of different lengths with roughly equal chances, will not optimally retrieve useful documents from such a collection. We present a modified technique— pivoted cosine normalization—that attempts to match the likelihood of retrieving documents of all lengths to the likelihood of their relevance, and show that this technique yields significant improvements in retrieval effectiveness.

Combining automatic and manual index representations in probabilistic retrieval

Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representations, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidential reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. In this article, we use a system based on this model to study the retrieval effectiveness benefits of combining the types of document and query information that are found in typical commercial databases and information services. The results indicate that substantial real benefits are possible. © 1995 John Wiley & Sons, Inc.