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1.
《Information processing & management》2005,41(5):1207-1223
This paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information retrieval (Overview of the TREC-2002 web track. In: Proceedings of the Eleventh Text Retrieval Conference TREC-2002, NIST Special Publication #500-251, 2003). These methods were applied on top of the basic information retrieval model as additional mechanisms to upgrade the system. Use of the title of web pages was found to be effective. It was confirmed that anchor texts of incoming links was beneficial as suggested in other works. Sentence–query similarity is a new type of information proposed by us and was identified to be the best information to take advantage of. Stratifying and re-ranking the retrieval list based on the maximum count of index terms in common between a sentence and a query resulted in significant improvement of performance. To demonstrate these facts a large-scale web information retrieval system was developed and used for experimentation. 相似文献
2.
应用领域本体的Web信息知识集成研究 总被引：2，自引：0，他引：2
缺少领域知识而进一步提高Web信息检索的质量是困难的，知识集成能够发挥重要作用。本文首先分析了目前Web用户信息利用的现状，研究领域本体与知识集成的方法，然后结合Web网页文档的特点及本体知识，给出一种基于领域本体的Web信息个性花集成方法，能够提高Web信息检索和用户利用的效率。 相似文献
3.
《Information processing & management》2016,52(2):273-298
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as “Philip Seymour Hoffman” where the results may require no recency at all. In this work, we focus on this type of queries named “time-sensitive queries” where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community. 相似文献
4.
A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called “DistanceRank” to compute ranks of web pages. The distance is defined as the number of “average clicks” between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley’s web for our experiments. 相似文献
5.
数字图书馆现有的检索引擎和检索技术由于无法提供上下文的语义信息,已经无法满足用户的检索需求。语义网技术可以很好的表达数字图书馆的内容,因此将语义网相关技术引入数字图书馆检索可以提高检索的精度。虽然数字图书馆的信息资源利用元数据表达并可以通过OAI-PMH进行访问,但是仍有很大部分需要语义网组件进行完善。在此基础上,提出了一个面向数字图书馆的通用模型的语义框架,这个框架可以满足用户高度个性化的信息需求。 相似文献
6.
Shubham Goel Ravinder Kumar Munish Kumar Vikram Chopra 《Information processing & management》2019,56(3):1053-1066
In the whole world, the internet is exercised by millions of people every day for information retrieval. Even for a small to smaller task like fixing a fan, to cook food or even to iron clothes persons opt to search the web. To fulfill the information needs of people, there are billions of web pages, each having a different degree of relevance to the topic of interest (TOI), scattered throughout the web but this huge size makes manual information retrieval impossible. The page ranking algorithm is an integral part of search engines as it arranges web pages associated with a queried TOI in order of their relevance level. It, therefore, plays an important role in regulating the search quality and user experience for information retrieval. PageRank, HITS, and SALSA are well-known page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a variant of SALSA to give sNorm(p) for the efficient ranking of web pages. Our approach relies on a p-Norm from Vector Norm family in a novel way for the ranking of web pages as Vector Norms can reduce the impact of low authority weight in hub weight calculation in an efficient way. Our study, then compares the rankings given by PageRank, HITS, SALSA, and sNorm(p) to the same pages in the same query. The effectiveness of the proposed approach over state of the art methods has been shown using performance measurement technique, Mean Reciprocal Rank (MRR), Precision, Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized DCG (NDCG). The experimentation is performed on a dataset acquired after pre-processing of the results collected from initial few pages retrieved for a query by the Google search engine. Based on the type and amount of in-hand domain expertise 30 queries are designed. The extensive evaluation and result analysis are performed using MRR, [email protected], MAP, DCG, and NDCG as the performance measuring statistical metrics. Furthermore, results are statistically verified using a significance test. Findings show that our approach outperforms state of the art methods by attaining 0.8666 as MRR value, 0.7957 as MAP value. Thus contributing to the improvement in the ranking of web pages more efficiently as compared to its counterparts. 相似文献
7.
《Information processing & management》2002,38(1):111-140
This research is a part of ongoing study to better understand citation analysis on the Web. It builds on Kleinberg's research (J. Kleinberg, R. Kumar, P. Raghavan, P. Rajagopalan, A. Tomkins, Invited survey at the International Conference on Combinatorics and Computing, 1999) that hyperlinks between web pages constitute a web graph structure and tries to classify different web graphs in the new coordinate space: out-degree, in-degree. The out-degree coordinate is defined as the number of outgoing web pages from a given web page. The in-degree coordinate is the number of web pages that point to a given web page. In this new coordinate space a metric is built to classify how close or far are different web graphs. Kleinberg's web algorithm (J. Kleinberg, Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 1998, pp. 668–677) on discovering “hub web pages” and “authorities web pages” is applied in this new coordinate space. Some very uncommon phenomenon has been discovered and new interesting results interpreted. This study does not look at enhancing web retrieval by adding context information. It only considers web hyperlinks as a source to analyze citations on the web. The author believes that understanding the underlying web page as a graph will help design better web algorithms, enhance retrieval and web performance, and recommends using graphs as a part of visual aid for search engine designers. 相似文献
8.
Metadata is designed to improve information organization and information retrieval effectiveness and efficiency on the Internet. The way web publishers respond to metadata and the way they use it when publishing their web pages, however, is still a mystery. The authors of this paper aim to solve this mystery by defining different professional publisher groups, examining the behaviors of these user groups, and identifying the characteristics of their metadata use. This study will enhance the current understanding of metadata application behavior and provide evidence useful to researchers, web publishers, and search engine designers. 相似文献
9.
《Information processing & management》2019,56(3):1026-1045
The study of query performance prediction (QPP) in information retrieval (IR) aims to predict retrieval effectiveness. The specificity of the underlying information need of a query often determines how effectively can a search engine retrieve relevant documents at top ranks. The presence of ambiguous terms makes a query less specific to the sought information need, which in turn may degrade IR effectiveness. In this paper, we propose a novel word embedding based pre-retrieval feature which measures the ambiguity of each query term by estimating how many ‘senses’ each word is associated with. Assuming each sense roughly corresponds to a Gaussian mixture component, our proposed generative model first estimates a Gaussian mixture model (GMM) from the word vectors that are most similar to the given query terms. We then use the posterior probabilities of generating the query terms themselves from this estimated GMM in order to quantify the ambiguity of the query. Previous studies have shown that post-retrieval QPP approaches often outperform pre-retrieval ones because they use additional information from the top ranked documents. To achieve the best of both worlds, we formalize a linear combination of our proposed GMM based pre-retrieval predictor with NQC, a state-of-the-art post-retrieval QPP. Our experiments on the TREC benchmark news and web collections demonstrate that our proposed hybrid QPP approach (in linear combination with NQC) significantly outperforms a range of other existing pre-retrieval approaches in combination with NQC used as baselines. 相似文献
10.
This paper presents a novel IR-style keyword search model for semantic web data retrieval, distinguished from current retrieval methods. In this model, an answer to a keyword query is a connected subgraph that contains all the query keywords. In addition, the answer is minimal because any proper subgraph can not be an answer to the query. We provide an approximation algorithm to retrieve these answers efficiently. A special ranking strategy is also proposed so that answers can be appropriately ordered. The experimental results over real datasets show that our model outperforms existing possible solutions with respect to effectiveness and efficiency. 相似文献
11.
Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods. 相似文献
12.
In Mongolian, two different alphabets are used, Cyrillic and Mongolian. In this paper, we focus solely on the Mongolian language using the Cyrillic alphabet, in which a content word can be inflected when concatenated with one or more suffixes. Identifying the original form of content words is crucial for natural language processing and information retrieval. We propose a lemmatization method for Mongolian. The advantage of our lemmatization method is that it does not rely on noun dictionaries, enabling us to lemmatize out-of-dictionary words. We also apply our method to indexing for information retrieval. We use newspaper articles and technical abstracts in experiments that show the effectiveness of our method. Our research is the first significant exploration of the effectiveness of lemmatization for information retrieval in Mongolian. 相似文献
13.
《Information processing & management》2020,57(2):102149
In test collection based evaluation of retrieval effectiveness, it has been suggested to completely avoid using human relevance judgments. Although several methods have been proposed, their accuracy is still limited. In this paper we present two overall contributions. First, we provide a systematic comparison of all the most widely adopted previous approaches on a large set of 14 TREC collections. We aim at analyzing the methods in a homogeneous and complete way, in terms of the accuracy measures used as well as in terms of the datasets selected, showing that considerably different results may be achieved considering different methods, datasets, and measures. Second, we study the combination of such methods, which, to the best of our knowledge, has not been investigated so far. Our experimental results show that simple combination strategies based on data fusion techniques are usually not effective and even harmful. However, some more sophisticated solutions, based on machine learning, are indeed effective and often outperform all individual methods. Moreover, they are more stable, as they show a smaller variation across datasets. Our results have the practical implication that, when trying to automatically evaluate retrieval effectiveness, researchers should not use a single method, but a (machine-learning based) combination of them. 相似文献
14.
Search engines and other text retrieval systems use high-performance inverted indexes to provide efficient text query evaluation. Algorithms for fast query evaluation and index construction are well-known, but relatively little has been published concerning update. In this paper, we experimentally evaluate the two main alternative strategies for index maintenance in the presence of insertions, with the constraint that inverted lists remain contiguous on disk for fast query evaluation. The in-place and re-merge strategies are benchmarked against the baseline of a complete re-build. Our experiments with large volumes of web data show that re-merge is the fastest approach if large buffers are available, but that even a simple implementation of in-place update is suitable when the rate of insertion is low or memory buffer size is limited. We also show that with careful design of aspects of implementation such as free-space management, in-place update can be improved by around an order of magnitude over a naïve implementation. 相似文献
15.
《Information processing & management》2023,60(1):103135
The pre-trained language models (PLMs), such as BERT, have been successfully employed in two-phases ranking pipeline for information retrieval (IR). Meanwhile, recent studies have reported that BERT model is vulnerable to imperceptible textual perturbations on quite a few natural language processing (NLP) tasks. As for IR tasks, current established BERT re-ranker is mainly trained on large-scale and relatively clean dataset, such as MS MARCO, but actually noisy text is more common in real-world scenarios, such as web search. In addition, the impact of within-document textual noises (perturbations) on retrieval effectiveness remains to be investigated, especially on the ranking quality of BERT re-ranker, considering its contextualized nature. To mitigate this gap, we carry out exploratory experiments on the MS MARCO dataset in this work to examine whether BERT re-ranker can still perform well when ranking text with noise. Unfortunately, we observe non-negligible effectiveness degradation of BERT re-ranker over a total of ten different types of synthetic within-document textual noise. Furthermore, to address the effectiveness losses over textual noise, we propose a novel noise-tolerant model, De-Ranker, which is learned by minimizing the distance between the noisy text and its original clean version. Our evaluation on the MS MARCO and TREC 2019–2020 DL datasets demonstrates that De-Ranker can deal with synthetic textual noise more effectively, with 3%–4% performance improvement over vanilla BERT re-ranker. Meanwhile, extensive zero-shot transfer experiments on a total of 18 widely-used IR datasets show that De-Ranker can not only tackle natural noise in real-world text, but also achieve 1.32% improvement on average in terms of cross-domain generalization ability on the BEIR benchmark. 相似文献
16.
R S Marcus 《Journal of the American Society for Information Science》1983,34(6):381-404
An experimental computer intermediary system, CONIT, that assists users in accessing and searching heterogeneous retrieval systems has been enhanced with various search aids. Controlled experiments have been conducted to compare the effectiveness of the enhanced CONIT intermediary with that of human expert intermediary search specialists. Some 16 end users, none of whom had previously operated either CONIT or any of the four connected retrieval systems, performed searches on 20 different topics using CONIT with no assistance other than that provided by CONIT itself (except to recover from computer/software bugs). These same users also performed searches on the same topics with the help of human expert intermediaries who searched using the retrieval systems directly. Sometimes CONIT and sometimes the human expert were clearly superior in terms of such parameters as recall and search time. In general, however, users searching alone with CONIT achieved somewhat higher online recall at the expense of longer session times. We conclude that advanced experimental intermediary techniques are now capable of providing search assistance whose effectiveness at least approximates that of human intermediaries in some contexts. Also analyzed is the cost effectiveness of current intermediary systems. Finally, consideration is given to the prospects for much more advanced systems which would perform such functions as automatic data-base selection and the simulation of human experts, and thereby make information retrieval more effective for all classes of users. 相似文献
17.
针对网络邮票图像的特点,提出邮票领域本体构建方法。根据网络邮票图像的视觉特征和描述文本．利用本体描述其语义特征,通过自动图像标注技术构建邮票图像本体库,并构建网络邮票图像的语义检索系统。实验表明,该系统解决了网络图像基于关键字检索和基于内容检索中的语义缺失问题,具有较高的图像检索准确率。 相似文献
18.
【目的/意义】查询意图歧义性对检索模型提出了挑战。针对查询意图歧义性程度,探讨了基于歧义程度的
多样化检索模型的检索效果。【方法/过程】将查询意图歧义性程度的表示方式分为序数变量或连续变量两种方式,
在此基础上,提出了基于三种排序策略的面向序数变量查询意图歧义性的多样化检索模型、基于查询重构的面向
连续变量查询意图歧义性的多样化检索模型,从而使得检索结果列表同时具有较高的覆盖率与新颖性。【结果/结
论】在公开数据集上,四个检索效果测评指标 α-nDCG@5、α-nDCG@10、α-nDCG@20 及 NRBP@20 表明,本文
提出的多样化检索模型优于基准实验,且获取准确的查询子主题能有效提升检索效果。【创新/局限】区分了查询意
图歧义性程度的两种表示方式,据此提出并验证了面向查询意图歧义性程度的多样化检索模型;然而限于实验运
行复杂程度,生成初始检索结果列表数据略少。 相似文献
19.
《Information processing & management》2022,59(5):103051
In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. In practice, a suitable workflow can provide guidelines for constructing a machine-mediated agent replacing of human agents. Therefore, we conduct a comparison analysis and summarize two challenges when directly applying the conversational agent workflow in web search to legal case retrieval: (1) It is complex for agents to express their understanding of users’ information need. (2) Selecting a candidate case from the SERPs is more difficult for agents, especially at the early stage of the search process. To tackle these challenges, we propose a suitable conversational agent workflow in legal case retrieval, which contains two additional key modules compared with that in web search: Query Generation and Buffer Mechanism. A controlled user experiment with three control groups, using the whole workflow or removing one of these two modules, is conducted. The results demonstrate that the proposed workflow can actually support conversational agents working more efficiently, and help users save search effort, leading to higher search success and satisfaction for legal case retrieval. We further construct a large-scale dataset and provide guidance on the machine-mediated conversational search system for legal case retrieval. 相似文献
20.
The estimation of query model is an important task in language modeling (LM) approaches to information retrieval (IR). The ideal estimation is expected to be not only effective in terms of high mean retrieval performance over all queries, but also stable in terms of low variance of retrieval performance across different queries. In practice, however, improving effectiveness can sacrifice stability, and vice versa. In this paper, we propose to study this tradeoff from a new perspective, i.e., the bias–variance tradeoff, which is a fundamental theory in statistics. We formulate the notion of bias–variance regarding retrieval performance and estimation quality of query models. We then investigate several estimated query models, by analyzing when and why the bias–variance tradeoff will occur, and how the bias and variance can be reduced simultaneously. A series of experiments on four TREC collections have been conducted to systematically evaluate our bias–variance analysis. Our approach and results will potentially form an analysis framework and a novel evaluation strategy for query language modeling. 相似文献