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Effective knowledge management in a knowledge-intensive environment can place heavy demands on the information filtering (IF) strategies used to model workers’ long-term task-needs. Because of the growing complexity of knowledge-intensive work tasks, a profiling technique is needed to deliver task-relevant documents to workers. In this study, we propose an IF technique with task-stage identification that provides effective codification-based support throughout the execution of a task. Task-needs pattern similarity analysis based on a correlation value is used to identify a worker’s task-stage (the pre-focus, focus formulation, or post-focus task-stage). The identified task-stage is then incorporated into a profile adaptation process to generate the worker’s current task profile. The results of a pilot study conducted in a research institute confirm that there is a low or negative correlation between search sessions and transactions in the pre-focus task-stage, whereas there is at least a moderate correlation between search sessions/transactions in the post-focus stage. Compared with the traditional IF technique, the proposed IF technique with task-stage identification achieves, on average, a 19.49% improvement in task-relevant document support. The results confirm the effectiveness of the proposed method for knowledge-intensive work tasks. 相似文献
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The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals’ information behavior. Classifying tasks in a domain has been viewed as a departure point of studies on the relationship between tasks and human information behavior. However, previous task classification schemes either classify tasks with respect to the requirements of specific studies or merely classify a certain category of task. Such approaches do not lead to a holistic picture of task since a task involves different aspects. Therefore, the present study aims to develop a faceted classification of task, which can incorporate work tasks and information search tasks into the same classification scheme and characterize tasks in such a way as to help people make predictions of information behavior. For this purpose, previous task classification schemes and their underlying facets are reviewed and discussed. Analysis identifies essential facets and categorizes them into Generic facets of task and Common attributes of task. Generic facets of task include Source of task, Task doer, Time, Action, Product, and Goal. Common attributes of task includes Task characteristics and User’s perception of task. Corresponding sub-facets and values are identified as well. In this fashion, a faceted classification of task is established which could be used to describe users’ work tasks and information search tasks. This faceted classification provides a framework to further explore the relationships among work tasks, search tasks, and interactive information retrieval and advance adaptive IR systems design. 相似文献
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《Information processing & management》2019,56(3):879-894
The documents retrieved by a web search are useful if the information they contain contributes to some task or information need. To measure search result utility, studies have typically focused on perceived usefulness rather than on actual information use. We investigate the actual usefulness of search results—as indicated by their use as sources in an extensive writing task—and the factors that make a writer successful at retrieving useful sources. Our data comprise 150 essays written by 12 writers whose querying, clicking and writing activities were recorded. By tracking authors’ text reuse behavior, we quantify the search results’ contribution to the task more accurately than before. We model the overall utility of the search results retrieved throughout the writing process using path analysis, and compare a binary utility model (Reuse Events) to one that quantifies a degree of utility (Reuse Amount). The Reuse Events model has greater explanatory power (63% vs. 48%); in both models, the number of clicks is by far the strongest predictor of useful results—with β-coefficients up to 0.7—while dwell time has a negative effect (β between −0.14 and −0.21). As a conclusion, we propose a new measure of search result usefulness based on a source’s contribution to an evolving text. Our findings are valid for tasks where text reuse is allowed, but also have implications on designing indicators of search result usefulness for general writing tasks. 相似文献
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Prior research in the social search space has focused on the informational benefits of collaborating with others during web and workplace information seeking. However, social interactions, especially during complex tasks, can have cognitive benefits as well. Our goal in this paper is to document the methods and outcomes of using social resources to help with exploratory search tasks. We used a talk-aloud protocol and video capture to explore the actions of eight subjects as they completed two “Google-hard” search tasks. Task questions were alternated between a Social and Non-Social Condition. The Social Condition restricted participants to use only social resources—search engines were not allowed. The Non-Social Condition permitted normal web-based information sources, but restricted the use of social tools. 相似文献
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Sven H. Koch Rumyana Proynova Barbara Paech Thomas Wetter 《Ethics and Information Technology》2013,15(1):45-61
Software users have different sets of personal values, such as benevolence, self-direction, and tradition. Among other factors, these personal values influence users’ emotions, preferences, motivations, and ways of performing tasks—and hence, information needs. Studies of user acceptance indicate that personal traits like values and related soft issues are important for the user’s approval of software. If a user’s dominant personal value were known, software could automatically show an interface variant which offers information and functionality that best matches his or her dominant value. A user’s dominant personal value is the one that most strongly influences his or her attitudes and behaviors. However, existing methods for measuring a user’s values are work intensive and/or interfere with the user’s privacy needs. If interface tailoring for very large groups of users is planned, value approximation has to be achieved on a large scale to assign individualized software to all users of the software. Our work focuses on approximating the dominant values of a user with less effort and less impact on privacy. Instead of probing for a user’s values directly, we explore the potential of approximating these values based on the user’s preferences for key tasks. Producing tailored versions of software is a separate topic not in the focus here. In this paper we rather describe a method to identify user values from task preferences and an empirical study of applying parts of this method. We are proposing the method in this paper for the first time except for a preliminary version orally presented at a workshop. The method consists of a research process and an application process. In the research process a researcher has to identify key tasks occurring in a context under investigation which have a relationship to personal values. These key tasks can be used in the application process to approximate the dominant values of new users in a similar context. In this empirical study we show that the research process of our method allows us to determine key tasks which approximate values in the shared context of nursing. The majority of the nurses were found to have one of the three following dominant values: benevolence, self-direction, or hedonism. Data confirmed common expectations: that nurses with the value of benevolence, when compared to all other nurses, had a higher preference for tasks which helped people immediately or improved their circumstances of the treatment. In relation to all other nurses, participants with self-direction disliked tasks which affected their personal freedom, and users with hedonism had a lower preference for tasks which involved physical work and preferred tasks which promised gratification. Our findings advance measurement of personal values in large user groups by asking questions with less privacy concern. However, the method requires substantial efforts during the initial research process to prepare such measurements. Future work includes replicating our method in other contexts and identifying value-dependent tasks for users with other values than the three values our empirical study mainly focused on. 相似文献
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《Information processing & management》2023,60(5):103453
Seeking online health information may reinforce the anxiety of those who are already overly anxious about their health. This study explored how people with health anxiety may behave differently in terms of their attentional biases when seeking health information online. We conducted an eye-tracking experiment with 17 participants in the high health anxious group and 17 participants in the low health anxious group, who performed three types of information-seeking tasks (factual, interpretive, and exploratory) on a Chinese health website. We observed that both groups mainly allocated their attention to the stages of evaluating the list of search results and synthesizing information to make health decisions. They showed similar attention tracks at the earlier search stages and health anxiety was found to associate with attentional biases towards certain website stimuli. However, the high health anxious group showed more active eye movements than their low health anxious counterparts. Attentional biases from the high health anxious group mainly occurred at the later stage of processing rather than the initial orientation stages. As for task types, the high health anxious group presented more extensive attentional biases when performing the interpretive task, compared to the explorative and factual tasks. The findings provide novel insights into the attentional biases of people with health anxiety as they search online for health information, which have implications on designing more effective information interventions for vulnerable groups of health information consumers. The findings can also help clinicians interpret patients’ anxiety-related sensations and provide intervening recommendations for clients in use of online health information. 相似文献
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《Information processing & management》2023,60(2):103173
Researchers have been aware that emotion is not one-hot encoded in emotion-relevant classification tasks, and multiple emotions can coexist in a given sentence. Recently, several works have focused on leveraging a distribution label or a grayscale label of emotions in the classification model, which can enhance the one-hot label with additional information, such as the intensity of other emotions and the correlation between emotions. Such an approach has been proven effective in alleviating the overfitting problem and improving the model robustness by introducing a distribution learning component in the objective function. However, the effect of distribution learning cannot be fully unfolded as it can reduce the model’s discriminative ability within similar emotion categories. For example, “Sad” and “Fear” are both negative emotions. To address such a problem, we proposed a novel emotion extension scheme in the prior work (Li, Chen, Xie, Li, and Tao, 2021). The prior work incorporated fine-grained emotion concepts to build an extended label space, where a mapping function between coarse-grained emotion categories and fine-grained emotion concepts was identified. For example, sentences labeled “Joy” can convey various emotions such as enjoy, free, and leisure. The model can further benefit from the extended space by extracting dependency within fine-grained emotions when yielding predictions in the original label space. The prior work has shown that it is more apt to apply distribution learning in the extended label space than in the original space. A novel sparse connection method, i.e., Leaky Dropout, is proposed in this paper to refine the dependency-extraction step, which further improves the classification performance. In addition to the multiclass emotion classification task, we extensively experimented on sentiment analysis and multilabel emotion prediction tasks to investigate the effectiveness and generality of the label extension schema. 相似文献
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Danushka Bollegala Tomokazu Goto Nguyen Tuan Duc Mitsuru Ishizuka 《Information processing & management》2013
Measuring the similarity between the semantic relations that exist between words is an important step in numerous tasks in natural language processing such as answering word analogy questions, classifying compound nouns, and word sense disambiguation. Given two word pairs (A, B) and (C, D), we propose a method to measure the relational similarity between the semantic relations that exist between the two words in each word pair. Typically, a high degree of relational similarity can be observed between proportional analogies (i.e. analogies that exist among the four words, A is to B such as C is to D). We describe eight different types of relational symmetries that are frequently observed in proportional analogies and use those symmetries to robustly and accurately estimate the relational similarity between two given word pairs. We use automatically extracted lexical-syntactic patterns to represent the semantic relations that exist between two words and then match those patterns in Web search engine snippets to find candidate words that form proportional analogies with the original word pair. We define eight types of relational symmetries for proportional analogies and use those as features in a supervised learning approach. We evaluate the proposed method using the Scholastic Aptitude Test (SAT) word analogy benchmark dataset. Our experimental results show that the proposed method can accurately measure relational similarity between word pairs by exploiting the symmetries that exist in proportional analogies. The proposed method achieves an SAT score of 49.2% on the benchmark dataset, which is comparable to the best results reported on this dataset. 相似文献
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Fatemeh Lashkari Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755
Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. 相似文献
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The use of non-English Web search engines has been prevalent. Given the popularity of Chinese Web searching and the unique characteristics of Chinese language, it is imperative to conduct studies with focuses on the analysis of Chinese Web search queries. In this paper, we report our research on the character usage of Chinese search logs from a Web search engine in Hong Kong. By examining the distribution of search query terms, we found that users tended to use more diversified terms and that the usage of characters in search queries was quite different from the character usage of general online information in Chinese. After studying the Zipf distribution of n-grams with different values of n, we found that the curve of unigram is the most curved one of all while the bigram curve follows the Zipf distribution best, and that the curves of n-grams with larger n (n = 3–6) had similar structures with β-values in the range of 0.66–0.86. The distribution of combined n-grams was also studied. All the analyses are performed on the data both before and after the removal of function terms and incomplete terms and similar findings are revealed. We believe the findings from this study have provided some insights into further research in non-English Web searching and will assist in the design of more effective Chinese Web search engines. 相似文献
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Partial least squares (PLSs) often require many latent variables (LVs) T to describe the variations in process variables X correlated with quality variables Y, which are obtained via the traditional nonlinear iterative PLS (NIPALS) optimal solution based on (X, Y). Total projection to latent structures (T-PLSs) performs further decomposition to extract LVs Ty directly related to Y from T, which are obtained by the PCA optimal solution based on the predicted value of Y. Inspired by T-PLS, combined with practical process characteristics, two fault detection approaches are proposed in this paper to solve problems encountered by T-PLS. Without the NIPALS, (X, Y) are projected into the latent variable space determined by main variations of Y directly. Furthermore, the structure and characteristics of several modified methods in statistical analysis are studied based on calculation procedures of solving PCA, PLS and T-PLS optimization problems, and the geometric significance of the T-PLS model is demonstrated in detail. Simulation analysis and case studies both indicate the effectiveness of the proposed approaches. 相似文献
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[目的/意义]探究差异性任务情境对用户移动诊疗信息搜索行为的影响，并基于对比分析为移动诊疗信息的产品和服务创新提供改进方法和策略。[方法/过程]选取事实型、解释型和探索型3种代表性任务情境，采用"情境实验+问卷访谈"的方法获取研究样本的行为特征数据并辅助以录屏软件全程记录实验过程，通过人工编码、数据清洗和数据预处理等步骤对收集到的问卷文本和视频数据进行处理，获得36个有效样本和108份有效数据并利用SPSS22.0进行Mann-Whitney非参数检验。[结果/结论]差异性任务情境对搜索过程变量均具有显著影响。相对于事实型任务，解释型任务和探索型任务不仅在查询串修改次数、访问网页数和点击链接数上显著更多，而且在搜索策略上也倾向于使用更多的搜索渠道。任务类型对用户在任务完成前后的感知任务难度、感知完成度和客观完成度等指标上有显著影响，其中事实型任务难度最低且客观完成度最高，任务类型对用户的任务认知和感知多渠道搜索有用性无显著影响。 相似文献
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This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages. 相似文献
15.
Michael J. Cole Jacek Gwizdka Chang Liu Nicholas J. Belkin Xiangmin Zhang 《Information processing & management》2013
The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n = 40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n = 409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session. 相似文献
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Despite a number of studies looking at Web experience and Web searching tactics and behaviours, the specific relationships between experience and cognitive search strategies have not been widely researched. This study investigates how the cognitive search strategies of 80 participants might vary with Web experience as they engaged in two researcher-defined tasks and two participant-defined information seeking tasks. Each of the two researcher-defined tasks and participant-defined tasks included a directed search task and a general-purpose browsing task. While there were almost no significant performance differences between experience levels on any of the four tasks, there were significant differences in the use of cognitive search strategies. Participants with higher levels of Web experience were more likely to use “Parallel player”, “Parallel hub-and-spoke”, “Known address search domain” and “Known address” strategies, whereas participants with lower levels of Web experience were more likely to use “Virtual tourist”, “Link-dependent”, “To-the-point”, “Sequential player”, “Search engine narrowing”, and “Broad first” strategies. The patterns of use and differences between researcher-defined and participant-defined tasks and between directed search tasks and general-purpose browsing tasks are also discussed, although the distribution of search strategies by Web experience were not statistically significant for each individual task. 相似文献
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《Information processing & management》2022,59(5):103040
In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to empower retrieval systems, recommender systems, and question answering systems, generating a surge in research interest. Recently developed reasoning methods usually suffer from poor performance when applied to incomplete or sparse KGs, due to the lack of evidential paths that can reach target entities. To solve this problem, we propose a hybrid multi-hop reasoning model with reinforcement learning (RL) called SparKGR, which implements dynamic path completion and iterative rule guidance strategies to increase reasoning performance over sparse KGs. Firstly, the model dynamically completes the missing paths using rule guidance to augment the action space for the RL agent; this strategy effectively reduces the sparsity of KGs, thus increasing path search efficiency. Secondly, an iterative optimization of rule induction and fact inference is designed to incorporate global information from KGs to guide the RL agent exploration; this optimization iteratively improves overall training performance. We further evaluated the SparKGR model through different tasks on five real world datasets extracted from Freebase, Wikidata and NELL. The experimental results indicate that SparKGR outperforms state-of-the-art baseline models without losing interpretability. 相似文献