シソーラス型文献検索システムにおける自動的重みづけ法の評価 : I.索引語への重みづけ

This paper gives a short overview of the Archimedes Network System, an information retrieval system that allows the user to browse, in a visual way, through domain knowledge about the topics discussed in the documents. The system was evaluated by medical students who used the system to find documents in Medline. The results were comparable with the results of WinSpirs, a Boolean retrieval system.

We introduce a new information retrieval model, the generalized vector space model (GVSM). In the GVSM, Boolean-like queries can be transformed into vectors spanned by the atoms of the free Boolean algebra generated by the index terms. Documents can, therefore, be ranked with respect to a query as in the conventional vector space model. Most significantly, the GVSM provides a unified view of the Boolean retrieval and vector-processing systems.

This paper is the second of the three parts of the work on the information retrieval systems proposed by Salton (see [13]). The system is defined by the notions of a partially ordered set of requests (A, ⩽), the set of documents X and a monotonic retrieval function U : A → 2X. Different conditions imposed on the set A and a function U make it possible to obtain various classes of information retrieval systems. We investigate systems in which (A, ⩽) is a partially ordered set, a lattice, a pseudo-Boolean algebra and a Boolean algebra. In my paper these systems are called partially ordered information retrieval systems (po-systems), lattice information retrieval systems (l-systems), pseudo-Boolean information retrieval systems (pB-systems) and Boolean information retrieval systems (B-systems). The first part concerned po-systems and l-systems. The second part deals with pB-systems and B-systems. In the third part systems with a partial access are investigated. The present part discusses the pB-systems and B-systems. The problems connected with the properties of sets of attributes are investigated. The method of constructing a selective system for a pB-system and a B-system is given. The problem related to the minimalization of systems are investigated.

An extended fuzzy linguistic approach to generalize boolean information retrieval

In this paper, we address there important issues with query expansion using a thesaurus; how to give weights to the terms in expanded queries, how to select additional search terms in the thesaurus, and how to enrich the terms in the manual thesaurus (namely, thesaurus reconstruction). To weight the terms in expanded queries, we construct the weighted thesaurus that has a similarity value between the terms in the thesaurus, using statistical co-occurrence in a corpus. To enrich the terms in the manual thesaurus, domain dependent terms which occur in a corpus are inserted into the weighted thesaurus using the co-occurrence information. In this paper, the reconstructed thesaurus with weights is defined as a domain-adapted, weighted thesaurus. Then we explain query expansion using the domain-adapted, weighted thesaurus in an extended Boolean retrieval model. To select additional search terms during query expansion, our model uses semi-automatic query expansion and a restriction method. In the experiments, our system had almost twice the recall of the boolean retrieval system not using the thesaurus or the query expansion retrieval system using the original thesaurus. And also, the precision of our system was almost the same precision as the other systems.

Now finding relevant information is absolutely needed by users of the information. Actually, finding relevant information is more difficult than just looking for information. The presence information retrieval (IR) system is a system for automatically searching the relevant information to the user needs, which is expressed in the required query, and this query will be used as an input and processed by IR system and then will get relevant documents to the required query. One of methods of finding relevant information by query is term weighting by both query and document. The commonly used methods are the term weighting of local and global weighting, while the local term weighting algorithms that are used such as TF, TF logarithmic, binary TF and augmented TF and global weighting is IDF, IDFP, IDFB. In this research, besides of comparison of term weighting algorithms, it is also proposed new improvement in term weighting algorithms by adding global weight value that changes base 10 logarithmic to base 2 logarithmic and adds the number 1 in the global term weighting IDFP. Measurements result of this research are Precision, Recall and NIAP. In This research, the best result for precision and NAIP have been performed by new propose term weighting algorithms. After compare and analyze the result of the weighting algorithms. It is necessary to combine the weighting with semantic algorithms so that it will get better result.

A major deficiency of traditional Boolean systems is their inability to represent the varying degrees to which a document may be written on a subject. In this article we isolate a number of criteria that should be met by any Boolean system generalized to have a weighting capability. It is proven that only one weighting rule satisfies these conditions—that associated with fuzzy‐set theory—and that this weighting scheme satisfies most of the other properties associated with Boolean algebra as well. Probabilistic weighting is then introduced as an alternative approach and the two systems compared. In the limit of zero/one weights, all systems considered converge to traditional Boolean retrieval.

Few studies have compared the interactive use of Boolean and natural language searching systems. We studied the use of three retrieval systems by senior medical students searching on queries generated by actual physicians in a clinical setting. The searchers were randomized to search on two of three different retrieval systems: a Boolean system, a word-based natural language system, and a concept-based natural language system. Our results showed no statistically significant differences in recall or precision among the three systems. Likewise, we found no user preference for any system over the others. In the course of this study we did find, however, a number of problems with traditional measures of retrieval evaluation when applied to the interactive search setting.

The experience of patients living with human-immunodeficiency virus/tuberculosis co-infection: a systematic review of qualitative evidence protocol

There exist noisy, unparallel sentences in parallel web pages. Web page structure is subjected to some limitation for sentences alignment task for web page text. The most straightforward way of aligning sentences is using a translation lexicon. However, a major obstacle to this approach is the lack of dictionary for training. This paper presents a method for automatically align Mongolian-Chinese parallel text on the Web via vector space model. Vector space model is an algebraic model for representing any object as vectors of identifiers, such as index terms. In the statistically based vector-space model, a sentence is conceptually represented by a vector of keywords extracted from the text. Extracted keywords are composed by content words, known as terms and the weight of a term in a sentence vector can be determined tf-idf method. CHI is used to compute the association between bilingual words. Once the term weights are determined, the similarity between sentence vectors is computed via cosine measure. The experimental results indicate that the method is accurate and efficient enough to apply without human intervention.

Classical approaches, namely term weighting and statistical document clustering do not score to achieve substantial breakthroughs in information retrieval research. Information retrieval systems must be designed to overcome the syntactical barrier in document representation. This paper discusses the combination of classical term weighting approaches and probabilistic inference concepts to form a powerful semantic document classification model. BRANT is a prototype adapting inference strategies from medical differential diagnosis to the area of information retrieval.

Most image retrieval systems only allow a fragment of text or an example image as a query. Most users have more complex information needs that are not easily expressed in either of these forms. This paper proposes a model based on the Inference Network framework from information retrieval that employs a powerful query language that allows structured query operators, term weighting, and the combination of text and images within a query. The model uses non-parametric methods to estimate probabilities within the inference network. Image annotation and retrieval results are reported and compared against other published systems and illustrative structured and weighted query results are given to show the power of the query language. The resulting system both performs well and is robust compared to existing approaches.KeywordsFeature VectorImage RetrievalLatent Dirichlet AllocationMean Average PrecisionRetrieval ResultThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The ability to effectively rank retrieved documents in order of their probable relevance to a query is a critical factor in statistically-based keyword retrieval systems. This paper summarizes a set of experiments with different methods of term weighting for documents, using measures of term importance within an entire document collection, term importance within a given document, and document length. It is shown that significant improvements over no term weighting can be made using a combination of weighting measures and normalizing for document length.

The main objective of an information retrieval system is to be effective in providing a user with relevant information in response to a query. However, especially given the information explosion which has created an enormous volume of information, efficiency issues cannot be ignored. Thus, to be able to quickly process lists of documents that have the keywords stated in a given query assigned/indexed to them by merging via the Boolean logic of the query is essential in a Boolean query system. A new algorithm, based loosely on concurrent codes, is developed and discussed.

In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach.KeywordsQuery TermWord SenseInformation Retrieval SystemWord Sense DisambiguationSemantic ClassThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.