Enhance Retrieval Effectiveness Research Articles

Statistical similarity matching and filtering for clinical image retrieval by machine learning approach

Clinical image retrieval plays a pivotal role in modern healthcare for diagnostics and research, but prior research has grappled with the challenge of achieving high accuracy due to limited filtering techniques. The proposed method includes statistical distance measurements for similarity comparison and a machine learning technique for image filtering. Throughout this framework, the search area for similarity matching is reduced by first filtering away irrelevant images using the probabilistic outcomes of the Support Vector Machine (SVM) classification as class predictions of search and database images. Resizing is done as part of the preprocessing. Then, using Principal Component Analysis (PCA), the preprocessed data’s textural features, visual characteristics, and low-level features are extracted. The study also suggested an adaptive similarity matching method centered on a linear integration of feature-level similarities on the individual-level level. The precision and ranking order details of the most appropriate images retrieved and predicted by SVMs are considered when calculating the feature weights. The system continually alters weights for every distinctive search to generate beneficial outcomes. The supervised and unsupervised learning strategies are studied to link low-level global image features in the generated PCA-based Eigen Space using their high-level semantic and visual classifications to reduce the semantic gap and enhance retrieval effectiveness. The ground-truth database used in experiments has 1594 unique medical images with 3 different databases. Our method significantly improves the precision and recall rates in image retrieval tasks by combining sophisticated feature extraction, data-driven algorithms, and deep learning models. Research obtained an impressive accuracy of 0.99, demonstrating the effectiveness of our approach. This novel methodology addresses the limitations of prior research and provides a robust and reliable solution for clinicians and researchers in the medical field seeking to access and analyze relevant clinical images.

Fuzzy Information Retrieval Based on Continuous Bag-of-Words Model

In this paper, we study the feasibility of performing fuzzy information retrieval by word embedding. We propose a fuzzy information retrieval approach to capture the relationships between words and query language, which combines some techniques of deep learning and fuzzy set theory. We try to leverage large scale data and the continuous-bag-of words model to find the relevant feature of words and obtain word embedding. To enhance retrieval effectiveness, we measure the relativity among words by word embedding, with the property of symmetry. Experimental results show that the recall ratio, precision ratio, and harmonic average of two ratios of the proposed method outperforms the ones of the traditional methods.

Towards enhancing retrieval effectiveness of search engines for diacritisized Arabic documents

The majority of Arabic text available on the web is written without short vowels (diacritics). Diacritics are commonly used in religious scripts such as the holy Quran (the book of Islam), Al-Hadith (the teachings of Prophet Mohammad (PBUH)), children's literature, and in some words where ambiguity of articulation might arise. Internet Arabic users might lose credible sources of Arabic text to be retrieved if they could not match the correct diacritical marks attached to the words in the collection. However, typing the diacritical marks is very annoying and time consuming. The other way around, is to ignore these marks and fall into the problem of ambiguity. Previous work suggested pre-processing of Arabic text to remove these diacritical marks before indexing. Consequently, there are noticeable discrepancies when searching the web for Arabic text using international search engines such as Google and yahoo. In this article, we propose a framework to enhance the retrieval effectiveness of search engines to search for diacritic and diacritic-less Arabic text through query expansion techniques. We used a rule-based stemmer and a semantic relational database compiled in an experimental thesaurus to do the expansion. We tested our approach on the scripts of the Quran. We found that query expansion for searching Arabic text is promising and it is likely that the efficiency can be further improved by advanced natural language processing tools.

Effect of relationships between words on Japanese information retrieval

Two Japanese-language information retrieval (IR) methods that enhance retrieval effectiveness by utilizing the relationships between words are proposed. The first method uses dependency relationships between words in a sentence. The second method uses proximity relationships, particularly information about the ordered co-occurrence of words in a sentence, to approximate the dependency relationships between them. A Structured Index has been constructed for these two methods, which represents the dependency relationships between words in a sentence as a set of binary trees. The Structured Index is created by morphological analysis and dependency analysis based on simple template matching and compound noun analysis derived from word statistics. Through retrieval experiments using the Japanese test collection for information retrieval systems (NTCIR-1, the NACSIS Test Collection for IR systems), it is shown that these two methods offer superior retrieval effectiveness compared with the TF--IDF method, and are effective with different databases and diverse search topics sets. There is little difference in retrieval effectiveness between these two methods.

Qualitative evaluation of automatic assignment of keywords to images

In image retrieval, most systems lack user-centred evaluation since they are assessed by some chosen ground truth dataset. The results reported through precision and recall assessed against the ground truth are thought of as being an acceptable surrogate for the judgment of real users. Much current research focuses on automatically assigning keywords to images for enhancing retrieval effectiveness. However, evaluation methods are usually based on system-level assessment, e.g. classification accuracy based on some chosen ground truth dataset. In this paper, we present a qualitative evaluation methodology for automatic image indexing systems. The automatic indexing task is formulated as one of image annotation, or automatic metadata generation for images. The evaluation is composed of two individual methods. First, the automatic indexing annotation results are assessed by human subjects. Second, the subjects are asked to annotate some chosen images as the test set whose annotations are used as ground truth. Then, the system is tested by the test set whose annotation results are judged against the ground truth. Only one of these methods is reported for most systems on which user-centred evaluation are conducted. We believe that both methods need to be considered for full evaluation. We also provide an example evaluation of our system based on this methodology. According to this study, our proposed evaluation methodology is able to provide deeper understanding of the system's performance.

Automatically constructing multi-relationship fuzzy concept networks for document retrieval

Although the knowledge bases incorporated in existing information retrieval systems can enhance retrieval effectiveness, many of them are built by domain experts. It is obvious that the construction of such knowledge bases requires a large amount of human effort. In this paper, an intelligent fuzzy information retrieval system with an automatically constructed knowledge base is presented; the knowledge base is represented by a multi-relationship fuzzy concept network. The multi-relationship fuzzy concept network can describe four kindsof context-independent and context-dependent fuzzy relationships, i.e., "fuzzy positive association" relationship, "fuzzy negative association" relationship, "fuzzygeneralization" relationship, and "fuzzy specialization" relationship between concepts. The users of the fuzzy information retrieval system can submit a fuzzy contextual query which specifies the search context in the query formula. The fuzzy information retrieval system retrieves documents whose contents are relevant to the user's query by some kinds of fuzzy relationships for the specified search context of the user's query. The proposed fuzzy information retrieval method is more intelligent and more flexible than the existing methods due to the fact that it can construct multi-relationship fuzzy concept networks automatically and it can provide contextual search capability to allow the users to specify fuzzy contextual queries in a more intelligent and flexible manner.

Mathematical, logical, and formal methods in information retrieval: An introduction to the special issue

AbstractResearch on the use of mathematical, logical, and formal methods, has been central to Information Retrieval research for long time. Research in this area is important not only because it helps enhancing retrieval effectiveness, but also because it helps clarifying the underlying concepts of Information Retrieval. In this article we outline some of the major aspects of the subject, and summarize the papers of this special issue with respect to how they relate to these aspects. We conclude by highlighting some directions of future research, which are needed to better understand the formal characteristics of Information Retrieval.

Automatic thesaurus for enhanced Chinese text retrieval

Asian languages such as Japanese, Korean and in particular Chinese, are beginning to gain popularity in the information retrieval (IR) domain. The quality of IR systems has traditionally been judged by the system’s retrieval effectiveness which, in turn, is commonly measured by data recall and data precision. This paper proposes and describes a process for generating an automatic Chinese thesaurus that can be used to provide related terms to a user’s queries to enhance retrieval effectiveness. In the absence of existing automatic Chinese thesauri, techniques used in English thesaurus generation have been evaluated and adapted to generate a Chinese equivalent. The automatic thesaurus is generated by computing the co‐occurrence values between domain‐specific terms found in a document collection. These co‐occurrence values are in turn derived from the term and document frequencies of the terms. A set of experiments was subsequently carried out on a document test set to evaluate the applicability of the thesaurus. Results obtained from these experiments confirmed that such an automatic generated thesaurus is able to improve the retrieval effectiveness of a Chinese IR system.

Employing multiple representations for Chinese information retrieval

For information retrieval in the Chinese language, three representation methods for texts are popular, namely: 1-gram or character, bigram, and short-word. Each has its advantages as well as drawbacks. Employing more than one method may combine advantages from them and enhance retrieval effectiveness. We investigated two ways of using them simultaneously: mixing representations in documents and queries, and combining retrieval lists obtained via different representations. The experiments were done with the 170 MB evaluated Chinese corpora and 54 long and short queries available from the (TREC) program and using our Probabilistic Indexing and Retrieval Components System (PIRCS retrieval system). Experiments show that good retrieval need not depend on accurate word segmentation; approximate segmentation into short-words will do. Results also show and confirm that bigram representation alone works well; mixing characters with bigram representation boosts effectiveness further, but it is preferable to mix characters with short-word indexing which is more efficient, needs less resource, and gives better retrieval more often. Combining retrieval lists from short-word with character representation and from bigram indexing provides the best retrieval results but also at a substantial cost.

An extended vector-processing scheme for searching information in hypertext systems

When searching information in a hypertext is limited to navigation, it is not an easy task, especially when the number of nodes and/or links becomes very large. A query-based access mechanism must be therefore provided to complement the navigational tools inherent in hypertext systems. Most mechanisms currently proposed are based on conventional information retrieval models which consider documents as independent entities, and ignore hypertext links. To promote the use of other information retrieval mechanisms adapted to hypertext systems, this study attempts to respond to the following questions: (1) How can we integrate information given by hypertext links into an information retrieval scheme? (2) Are these hypertext links (and link semantics) clues to the enhancement of retrieval effectiveness? (3) If so, how can we use them? Two solutions are: (a) using a default weight function based on link type or assigning the same strength to all link types; or (b) using a specific weight for each particular link, i.e. the level of association or a similarity measure. This study proposes an extended vector-processing scheme which extracts additional information from hypertext links to enhance retrieval effectiveness. To carry out our investigations, we have built a hypertext based on two medium-size collections, the cacm and the cisi collection. The hypergraph is composed of explicit links (bibliographic references), computed links based on bibliographic information (bibliographic coupling, co-citation), or on hypertext links established according to document representatives (nearest neighbor).

A new probabilistic scheme for information retrieval in hypertext

Abstract The aim of probabilistic models is to define a retrieval strategy within which documents can be optimally ranked according to their relevance probability, with respect to a given request. In this scheme, the underlying probabilities are estimated according to a history of past queries along with their relevance judgments. Having evolved over the last twenty years, these estimations allow us to take both document frequency and within-document frequency into account. In the current study, we suggest representing documents not only by index term vectors as proposed by previous probabilistic models but also by considering relevance hypertext links. These relationships, which provide additional evidence on document content, are established according to requests and relevance judgments, and may improve the ranking of the retrieved records, in a sequence most likely to fulfill user intent. Thus, to enhance retrieval effectiveness, our learning retrieval scheme should modify: (1) the weight assigned to e...

A translating computer interface for end‐user operation of heterogeneous retrieval systems. II. Evaluations

AbstractThe evaluation of the concept of a translating computer interface for simplifying operation of multiple, heterogeneous online bibliographic retrieval systems has been undertaken. An experimental retrieval system, named CONIT, was built and tested under controlled conditions with inexperienced end users. A detailed analysis of the experimental usages showed that users were able to master interface operation sufficiently well to find relevant document references. Success was attributed, in part, to a simple command language, adequate online instruction, and a simplified natural‐language, keyword/stem approach to searching. It is concluded that operational interfaces of the type studied can provide for increased usability of existing systems in a cost effective manner, especially for inexperienced end users who cannot easily avail themselves of expert intermediary searchers. Furthermore, more advanced interfaces based on improved instruction and automated search strategy techniques could further enhance retrieval effectiveness for a wide class of users.