Search (8 results, page 1 of 1)

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Abstract

This paper provides a brief overview of the development and current status of the Cataloging-in-Publication (CIP) project in China. The China CIP project is a new one implemented in 1993. In the paper, the development of CIP in the world is described, followed by when and how it was introduced into China. The paper tells the significances of CIP in detail. The implementation of the CIP project and differences of CIP work in China from that in the United States are also reflected here. Finally, the contribution discusses the problems in implementing the project and suggests ways to solve them. The project combines the publishing house, library, and distributor into the document information system. CIP is not only a kind of cataloging, but also a bond among them. It is believed that the CIP project in China has a bright future.

Source

Cataloging and classification quarterly. 22(1996) no.1, S.69-76

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Abstract

Purpose - With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach - The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings - Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level. Originality/value - Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

Date

20. 1.2015 18:30:22

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Abstract

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.

Date

22. 6.2023 14:55:20

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Abstract

User domain knowledge affects search behaviors and search success. Predicting a user's knowledge level from implicit evidence such as search behaviors could allow an adaptive information retrieval system to better personalize its interaction with users. This study examines whether user domain knowledge can be predicted from search behaviors by applying a regression modeling analysis method. We identify behavioral features that contribute most to a successful prediction model. A user experiment was conducted with 40 participants searching on task topics in the domain of genomics. Participant domain knowledge level was assessed based on the users' familiarity with and expertise in the search topics and their knowledge of MeSH (Medical Subject Headings) terms in the categories that corresponded to the search topics. Users' search behaviors were captured by logging software, which includes querying behaviors, document selection behaviors, and general task interaction behaviors. Multiple regression analysis was run on the behavioral data using different variable selection methods. Four successful predictive models were identified, each involving a slightly different set of behavioral variables. The models were compared for the best on model fit, significance of the model, and contributions of individual predictors in each model. Each model was validated using the split sampling method. The final model highlights three behavioral variables as domain knowledge level predictors: the number of documents saved, the average query length, and the average ranking position of the documents opened. The results are discussed, study limitations are addressed, and future research directions are suggested.

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Date

22. 6.2023 18:18:34

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Abstract

This paper introduces a hybrid method for abstracting Chinese text. It integrates the statistical approach with language understanding. Some linguistics heuristics and segmentation are also incorporated into the abstracting process. The prototype system is of a multipurpose type catering for various users with different reqirements. Initial responses show that the proposed method contributes much to the flexibility and accuracy of the automatic Chinese abstracting system. In practice, the present work provides a path to developing an intelligent Chinese system for automating the information

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Abstract

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

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Abstract

Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors. This study looks at users' dwelling behavior on documents and several contextual factors: the stage of users' work tasks, task type, and users' knowledge of task topics, to explore whether or not taking account contextual factors could help infer document usefulness from dwell time. A controlled laboratory experiment was conducted with 24 participants, each coming 3 times to work on 3 subtasks in a general work task. The results show that task stage could help interpret certain types of dwell time as reliable indicators of document usefulness in certain task types, as was topic knowledge, and the latter played a more significant role when both were available. This study contributes to a better understanding of how dwell time can be used as implicit evidence of document usefulness, as well as how contextual factors can help interpret dwell time as an indicator of usefulness. These findings have both theoretical and practical implications for using behaviors and contextual factors in the development of personalization systems.