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1.
This study introduces a novel framework for evaluating passage and XML retrieval. The framework focuses on a user’s effort
to localize relevant content in a result document. Measuring the effort is based on a system guided reading order of documents.
The effort is calculated as the quantity of text the user is expected to browse through. More specifically, this study seeks
evaluation metrics for retrieval methods following a specific fetch and browse approach, where in the fetch phase documents
are ranked in decreasing order according to their document score, like in document retrieval. In the browse phase, for each
retrieved document, a set of non-overlapping passages representing the relevant text within the document is retrieved. In
other words, the passages of the document are re-organized, so that the best matching passages are read first in sequential
order. We introduce an application scenario motivating the framework, and propose sample metrics based on the framework. These
metrics give a basis for the comparison of effectiveness between traditional document retrieval and passage/XML retrieval
and illuminate the benefit of passage/XML retrieval. 相似文献
2.
3.
In many probabilistic modeling approaches to Information Retrieval we are interested in estimating how well a document model
“fits” the user’s information need (query model). On the other hand in statistics, goodness of fit tests are well established
techniques for assessing the assumptions about the underlying distribution of a data set. Supposing that the query terms are
randomly distributed in the various documents of the collection, we actually want to know whether the occurrences of the query
terms are more frequently distributed by chance in a particular document. This can be quantified by the so-called goodness
of fit tests. In this paper, we present a new document ranking technique based on Chi-square goodness of fit tests. Given
the null hypothesis that there is no association between the query terms q and the document d irrespective of any chance occurrences, we perform a Chi-square goodness of fit test for assessing this hypothesis and calculate
the corresponding Chi-square values. Our retrieval formula is based on ranking the documents in the collection according to
these calculated Chi-square values. The method was evaluated over the entire test collection of TREC data, on disks 4 and
5, using the topics of TREC-7 and TREC-8 (50 topics each) conferences. It performs well, outperforming steadily the classical
OKAPI term frequency weighting formula but below that of KL-Divergence from language modeling approach. Despite this, we believe
that the technique is an important non-parametric way of thinking of retrieval, offering the possibility to try simple alternative
retrieval formulas within goodness-of-fit statistical tests’ framework, modeling the data in various ways estimating or assigning any arbitrary theoretical distribution
in terms. 相似文献
4.
We propose a method for performing evaluation of relevance feedback based on simulating real users. The user simulation applies
a model defining the user’s relevance threshold to accept individual documents as feedback in a graded relevance environment;
user’s patience to browse the initial list of retrieved documents; and his/her effort in providing the feedback. We evaluate
the result by using cumulated gain-based evaluation together with freezing all documents seen by the user in order to simulate
the point of view of a user who is browsing the documents during the retrieval process. We demonstrate the method by performing
a simulation in the laboratory setting and present the “branching” curve sets characteristic for the presented evaluation
method. Both the average and topic-by-topic results indicate that if the freezing approach is adopted, giving feedback of
mixed quality makes sense for various usage scenarios even though the modeled users prefer finding especially the most relevant
documents. 相似文献
5.
Anchor texts complement Web page content and have been used extensively in commercial Web search engines. Existing methods
for anchor text weighting rely on the hyperlink information which is created by page content editors. Since anchor texts are
created to help user browse the Web, browsing behavior of Web users may also provide useful or complementary information for
anchor text weighting. In this paper, we discuss the possibility and effectiveness of incorporating browsing activities of
Web users into anchor texts for Web search. We first make an analysis on the effectiveness of anchor texts with browsing activities.
And then we propose two new anchor models which incorporate browsing activities. To deal with the data sparseness problem
of user-clicked anchor texts, two features of user’s browsing behavior are explored and analyzed. Based on these features,
a smoothing method for the new anchor models is proposed. Experimental results show that by incorporating browsing activities
the new anchor models outperform the state-of-art anchor models which use only the hyperlink information. This study demonstrates
the benefits of Web browsing activities to affect anchor text weighting. 相似文献
6.
Jovan Pehcevski James A. Thom Anne-Marie Vercoustre Vladimir Naumovski 《Information Retrieval》2010,13(5):568-600
Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity
extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked
list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated
on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in
answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure
of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as
answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic
difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition
to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the
optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories
and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction
is a promising approach that could also be exploited to further improve the entity ranking performance. 相似文献
7.
Ben Carterette 《Information Retrieval》2011,14(1):89-106
A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics
related to the user’s information need, or contain nuggets of information with little redundancy. Evaluation measures have
been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation
time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for
document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation
to investigate the practical implications of this for optimization and evaluation of retrieval systems. 相似文献
8.
When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering. 相似文献
9.
[目的/意义]现有新闻文档实体排序研究大多以文档或实体为中心,如文本分类、实体链接等,关注实体在文本中的重要性的研究较少,本研究探讨基于重要性的新闻文档实体排序。[方法/过程]给定一篇文档,判断文档中实体相对文档而言的重要性,并基于此对实体进行排序。在搜狗全网新闻数据集上进行实验,并利用NDCG和逆序对比率两个指标对实体排序结果进行评价。[结果/结论]实验结果表明,基于实体频率、TF*IDF、信息熵、TextRank等的方法以及集成方法都达到了较好的效果,基于聚集系数的方法效果一般。其中基于TF*IDF的方法NDCG值为95.86%,是该指标下的最好结果;基于集成方法的逆序对比率值为84.46%,是该指标下的最好结果。 相似文献
10.
提出一种基于概念格的数字图书馆用户检索行为序列模式挖掘方法。该方法采用“基于概念格的自顶向下与分治相结合”的挖掘思想,通过自顶向下的概念格迭代,利用概念格的复用性和提取频繁项集的优势,获得数字图书馆用户检索行为的序列模式。该方法不需要遍历原始用户信息数据库,能够大大压缩挖掘时间,有助于数字图书馆提高用户检索速度、改进个性化服务。 相似文献
11.
Mark Boukes Hajo G. Boomgaarden Marjolein Moorman Claes H. De Vreese 《Mass Communication and Society》2014,17(3):354-378
Opinionated news targets communities of likeminded viewers, relies on dramaturgical storytelling techniques, and shares characteristics with political satire. Accordingly, opinionated news should be understood as a specific form of political entertainment. We have investigated the mechanisms underlying the effects of opinionated news on political attitudes using an experimental design that employed manipulated television news items. Findings confirm that opinionated news positively affects policy attitudes via its presumed influence on others and subsequent perceptions of the opinion climate. However, opinionated news also negatively affects attitudes via hostile media perceptions and evoked anger, especially for people with incongruent political preferences. Due to these opposing processes, we found no total effect of opinionated news on policy attitudes. Conditions are discussed under which either the positive or the negative indirect effect is likely to dominate. 相似文献
12.
Server selection is an important subproblem in distributed information retrieval (DIR) but has commonly been studied with
collections of more or less uniform size and with more or less homogeneous content. In contrast, realistic DIR applications
may feature much more varied collections. In particular, personal metasearch—a novel application of DIR which includes all
of a user’s online resources—may involve collections which vary in size by several orders of magnitude, and which have highly
varied data. We describe a number of algorithms for server selection, and consider their effectiveness when collections vary
widely in size and are represented by imperfect samples. We compare the algorithms on a personal metasearch testbed comprising
calendar, email, mailing list and web collections, where collection sizes differ by three orders of magnitude. We then explore
the effect of collection size variations using four partitionings of the TREC ad hoc data used in many other DIR experiments.
Kullback-Leibler divergence, previously considered poorly effective, performs better than expected in this application; other
techniques thought to be effective perform poorly and are not appropriate for this problem. A strong correlation with size-based
rankings for many techniques may be responsible. 相似文献
13.
[目的/意义] 在专利分析中引入Knowledge Graph,将专利内容转换为由Knowledge Graph中实体语义关系所构成的图结构,进而探索该形式的专利表示方法在识别专利诉讼案中专利证据的可行性。[方法/过程] 在专利内容转换过程中,首先采用自动术语识别方法提取其实体指称,并通过实体链接将实体指称转化为命名实体,进而根据图算法识别出该专利的隐含实体,最终形成该专利所对应的图结构。[结果/结论] 将该专利表示方式应用于硬盘驱动器领域来寻找专利诉讼案中可用的证据专利,实证结果表明,与当前主流的专利文本表示方式相比,该方法在寻找证据专利效果上有较大提升。 相似文献
14.
User generated content forms an important domain for mining knowledge. In this paper, we address the task of blog feed search:
to find blogs that are principally devoted to a given topic, as opposed to blogs that merely happen to mention the topic in
passing. The large number of blogs makes the blogosphere a challenging domain, both in terms of effectiveness and of storage
and retrieval efficiency. We examine the effectiveness of an approach to blog feed search that is based on individual posts
as indexing units (instead of full blogs). Working in the setting of a probabilistic language modeling approach to information
retrieval, we model the blog feed search task by aggregating over a blogger’s posts to collect evidence of relevance to the
topic and persistence of interest in the topic. This approach achieves state-of-the-art performance in terms of effectiveness.
We then introduce a two-stage model where a pre-selection of candidate blogs is followed by a ranking step. The model integrates
aggressive pruning techniques as well as very lean representations of the contents of blog posts, resulting in substantial
gains in efficiency while maintaining effectiveness at a very competitive level. 相似文献
15.
Michael Cader 《Publishing Research Quarterly》2008,24(4):240-250
An account of the process of constant experimentation that drives our industry-leading newsletters and web site; the lessons
behind our entrepreneurial experiences that should be meaningful to many within book publishing; and suggestions for how publishers
of all sizes can employ a similar mindset in getting closer to readers and leveraging their natural assets for success in
today’s rapidly changing marketplace.
This paper was presented during the “Making Information Pay 2008” conference organized by the Book Industry Study Group, as
a different kind of text as far as I am concerned than an essay. What I actually said the day of the presentation was undoubtedly
somewhat different than what is written here. 相似文献
16.
旨在实现对给定的实体对象集匹配出尽可能宽的实体对象面,以帮助用户快速找到相关信息,尤其是那些需动态整合的特定领域的语义关联信息。分析Web文档中的实体对象结构及其关系,并借助Schema.org方案中的语义分类思想,提出构建具有语义特性的实体对象数据库建设方案。基于该数据库提出一个自适应的实体对象检索框架,该框架能对用户的查询意图进行分析并进行语义分类,形成一条条涵盖实体对象的查询语句,接着“智能地”选择、执行某些具有优先权的查询语句以匹配出那些保存在事实数据库中的相关实体对象。本研究旨在一定程度上实现“滚雪球”式的高效检索思想,满足智能检索技术的需求,促进以实体对象作为研究对象的情报理论研究工作的开展,并为智能情报检索技术的应用规划提供有用参考。 相似文献
17.
李明伍 《现代图书情报技术》2010,26(4):92-96
介绍图书馆电子资源OpenSearch插件的开发,较好地实现图书馆资源与用户的浏览环境和检索习惯的集成融汇,满足用户方便、快捷、高效地利用资源的需求。 相似文献
18.
[目的/意义] 探索科研命名实体及其关系的识别与抽取，提升其在长句等复杂情况下的识别效果，为进一步的应用提供参考与借鉴。[方法/过程] 以依存句法特征分析为基础，提出一种科研命名实体关系抽取方法，过程包括：①使用Standford Tagger工具对目标文本进行词性标注；②基于标注结果，围绕核心谓词和SAO结构，将目标文本分割为结构规范的语义片段；③通过依存句法分析，找出与核心谓词语义相关的主语和宾语，构成（实体，关系，实体）三元组。[结果/结论] 与Ollie、Reverb等主流算法进行的对比测试表明，该方法可以有效提升科研命名实体识别的准确性。 相似文献
19.
以世界著名的三大检索系统SCI、EI、ISTP为数据源，按文献计量学的科学计量指标对电子科技大学在学术期刊上发表论文的情况进行统计与分析，包括电子科技大学论文数及其在全国高校中的排名，电子科技大学论文的学科分布、学院分布和作者群的分布。从一个重要侧面反映了高校的学科水平，为高校的学科建设、科研评估提供情报依据。 相似文献
20.
Robin Aly Aiden Doherty Djoerd Hiemstra Franciska de Jong Alan F. Smeaton 《Information Retrieval》2013,16(5):557-583
Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance. 相似文献