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Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation 将场景知识整合到事件表示的统一微调体系结构中
Jianming Zheng, Fei Cai, Honghui Chen
Given an occurred event, human can easily predict the next event or reason the preceding event, yet which is difficult for machine to perform such event reasoning. Event representation bridges the connection and targets to model the process of event reasoning as a machine-readable format, which then can support a wide range of applications in information retrieval, e.g., question answering and information extraction. Existing work mainly resorts to a joint training to integrate all levels of training loss in event chains by a simple loss summation, which is easily trapped into a local optimum. In addition, the scenario knowledge in event chains is not well investigated for event representation. In this paper, we propose a unified fine-tuning architecture, incorporated with scenario knowledge for event representation, i.e., UniFA-S, which mainly consists of a unified fine-tuning architecture (UniFA) and a scenario-level variational auto-encoder (S-VAE). In detail, UniFA employs a multi-step fine-tuning to integrate all levels of training and S-VAE applies a stochastic variable to implicitly represent the scenario-level knowledge. We evaluate our proposal from two aspects, i.e., the representation and inference abilities. For the representation ability, our ensemble model UniFA-S can beat state-of-the-art baselines for two similarity tasks. For the inference ability, UniFA-S can outperform the best baseline, achieving 4.1%-8.2% improvements in terms of accuracy for various inference tasks.
给定一个已经发生的事件,人类可以很容易地预测下一个事件或对前一个事件进行推理,而机器很难进行这样的事件推理。事件表示架起了连接的桥梁,目标是将事件推理过程建模为机器可读的格式,从而可以支持信息检索中的广泛应用,例如问答和信息提取。现有工作主要采用联合训练的方式,通过简单的损失求和来整合事件链中各级训练损失,容易陷入局部最优。此外,对于事件表示,事件链中的场景知识还没有得到很好的研究。本文提出了一种结合场景知识进行事件表示的统一微调架构,即UniFA- s,主要由统一微调架构(UniFA)和场景级变分自编码器(S-VAE)组成。具体而言,UniFA采用多步微调来整合所有级别的训练,S-VAE采用随机变量来隐式表示场景级知识。我们从表征能力和推理能力两个方面来评价我们的提议。对于表示能力,我们的集成模型UniFA-S可以在两个相似任务上击败最先进的基线。在推理能力方面,UniFA-S可以超越最佳基线,在各种推理任务上的准确率提高了4.1%-8.2%。
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引用次数: 18
Investigating Reference Dependence Effects on User Search Interaction and Satisfaction: A Behavioral Economics Perspective 参考依赖对用户搜索交互和满意度的影响:行为经济学视角
Jiqun Liu, Fangyuan Han
How users think, behave, and make decisions when interacting with information retrieval (IR) systems is a fundamental research problem in the area of Interactive IR. There is substantial evidence from behavioral economics and decision sciences demonstrating that in the context of decision-making under uncertainty, the carriers of value behind actions are gains and losses defined relative to a reference point, rather than the absolute final outcomes. This Reference Dependence Effect as a systematic cognitive bias was largely ignored by most formal interaction models built upon a series of unrealistic assumptions of user rationality. To address this gap, our work seeks to 1) understand the effects of reference points on search behavior and satisfaction at both query and session levels; 2) apply the knowledge of reference dependence in predicting users' search decisions and variations in level of satisfaction. Based on our experiments on three datasets collected from 1840 task-based search sessions (5225 query segments), we found that: 1) users' search satisfaction and many aspects of search behaviors and decisions are significantly associated with relative gains, losses and the associated reference points; 2) users' judgments of session-level satisfaction are significantly affected by peak and end reference moments; 3) compared to final-outcome-based baselines, models employing gain- and loss-based features often achieve significantly better performances in predicting search decisions and user satisfaction. The adaptation of behavioral economics perspective enables us to keep taking advantage of the collision of interdisciplinary insights in advancing IR research and also increase the explanatory power of formal search models by providing them with a more realistic behavioral and psychological foundation.
用户在与信息检索系统交互时如何思考、行为和决策是交互式信息检索领域的一个基本研究问题。行为经济学和决策科学的大量证据表明,在不确定的决策背景下,行动背后的价值载体是相对于参考点定义的收益和损失,而不是绝对的最终结果。这种参考依赖效应作为一种系统性的认知偏差,在很大程度上被大多数建立在一系列不切实际的用户理性假设之上的正式交互模型所忽视。为了解决这一差距,我们的工作旨在1)了解参考点对查询和会话级别的搜索行为和满意度的影响;2)运用参考依赖知识预测用户的搜索决策和满意度的变化。基于对1840个任务搜索会话(5225个查询段)的3个数据集的实验,我们发现:1)用户的搜索满意度以及搜索行为和决策的许多方面与相对收益、损失和相关参考点显著相关;2)用户对会话级满意度的判断受到峰值和终点参考矩的显著影响;3)与基于最终结果的基线相比,采用基于增益和损失特征的模型通常在预测搜索决策和用户满意度方面取得了显著更好的性能。行为经济学视角的适应使我们能够继续利用跨学科见解的碰撞来推进IR研究,并通过为正式搜索模型提供更现实的行为和心理基础来增强其解释力。
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引用次数: 17
Immersive Search: Using Virtual Reality to Examine How a Third Dimension Impacts the Searching Process 沉浸式搜索:使用虚拟现实来检查第三维度如何影响搜索过程
Austin R. Ward, Robert G. Capra
In this paper, we present results from an exploratory study to investigate users' behaviors and preferences for three different styles of search results presentation in a virtual reality (VR) head-mounted display (HMD). Prior work in 2D displays has suggested possible benefits of presenting information in ways that exploit users' spatial cognition abilities. We designed a VR system that displays search results in three different spatial arrangements: a list of 8 results, a 4x5 grid, and a 2x10 arc. These spatial display conditions were designed to differ in terms of the number of results displayed per page (8 vs 20) and the amount of head movement required to scan the results (list < grid < arc). Thirty-six participants completed 6 search trials in each display condition (18 total). For each trial, the participant was presented with a display of search results and asked to find a given target result or to indicate that the target was not present. We collected data about users' behaviors with and perceptions about the three display conditions using interaction data, questionnaires, and interviews. We explore the effects of display condition and target presence on behavioral measures (e.g., completion time, head movement, paging events, accuracy) and on users' perceptions (e.g., workload, ease of use, comfort, confidence, difficulty, and lostness). Our results suggest that there was no difference in accuracy among the display conditions, but that users completed tasks more quickly using the arc. However, users also expressed lower preferences for the arc, instead preferring the list and grid displays. Our findings extend prior research on visual search into to the area of 3-dimensional result displays for interactive information retrieval in VR HMD environments.
在本文中,我们介绍了一项探索性研究的结果,该研究调查了用户在虚拟现实(VR)头戴式显示器(HMD)中对三种不同风格的搜索结果显示的行为和偏好。先前在二维显示器上的工作表明,以利用用户空间认知能力的方式呈现信息可能会带来好处。我们设计了一个VR系统,它以三种不同的空间排列方式显示搜索结果:8个结果列表,4x5网格和2x10弧线。这些空间显示条件被设计成在每页显示的结果数量(8 vs 20)和扫描结果所需的头部移动量(列表<网格<圆弧)方面有所不同。36名参与者在每种显示条件下完成6次搜索试验(共18次)。在每次试验中,参与者都会看到搜索结果的显示,并被要求找到一个给定的目标结果,或者指出目标不存在。我们使用交互数据、问卷调查和访谈收集了有关用户对三种显示条件的行为和看法的数据。我们探讨了显示条件和目标存在对行为测量(例如,完成时间,头部运动,分页事件,准确性)和用户感知(例如,工作量,易用性,舒适性,信心,难度和迷失)的影响。我们的研究结果表明,在不同的显示条件下,准确度没有差异,但使用弧线的用户完成任务的速度更快。然而,用户对圆弧的偏好也较低,他们更喜欢列表和网格显示。我们的研究结果将先前的视觉搜索研究扩展到VR HMD环境中交互式信息检索的三维结果显示领域。
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引用次数: 9
Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine 基于知识图谱的可解释精准医学生物医学信息检索
Zuoxi Yang
As for many complex diseases, there is no "one size fits all" solutions for patients with a particular diagnosis in practice, which should be treated depends on patient's genetic, environmental, lifestyle choices and so on. Precision medicine can provide personalized treatment for a particular patient that has been drawn more and more attention. There are a large number of treatment options, which is overwhelming for clinicians to make best treatment for a particular patient. One of the effective ways to alleviate this problem is biomedical information retrieval system, which can automatically find out relevant information and proper treatment from mass of alternative treatments and cases. However, in the biomedical literature and clinical trials, there is a larger number of synonymous, polysemous and context terms, causing the semantic gap between query and document in traditional biomedical information retrieval systems. Recently, deep learning-based biomedical information retrieval systems have been adopted to address this problem, which has the potential improvements in the performance of BMIR. With these approaches, the semantic information of query and document would be encoded as low-dimensional feature vectors. Although most existing deep learning-based biomedical information retrieval systems can perform strong accuracy, they are usually treated as a black-box model that lack the explainability. It would be difficult for clinicians to understand their ranked results, which make them doubt the effectiveness of these systems. Reasonable explanations are profitable for clinicians to make better decisions via appropriate treatment logic inference, thus further enhancing the transparency, fairness and trust of biomedical information retrieval systems. Furthermore, knowledge graph has drawn more and more attention which contains abundant real-world facts and entities. It is an effective way to provide accuracy and explainability for deep learning model and reduce the knowledge gap between experts and publics. However, it is usually simply employed as a query expansion strategy simply into biomedical information retrieval systems. It remains an open question how to extend explainable biomedical information retrieval systems to knowledge graph. Given the above, to alleviate the tradeoff between accuracy and explainability of the precision medicine, we propose to research on Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine. In this work, we propose a neural-based biomedical information retrieval model to address the semantic gap problem and fully investigate the utility of KG for the explainable biomedical information retrieval systems. which can soft-matches the query and document with semantic information instead of ranking the model by exact matches. On the one hand, our model encodes semantic feature information of documents by using convolutional neural networks, which perform strong ability t
对于许多复杂的疾病,在实践中并没有针对特定诊断的患者“一刀切”的解决方案,应该根据患者的遗传、环境、生活方式选择等进行治疗。精准医疗可以为特定患者提供个性化治疗,这一点越来越受到人们的关注。有大量的治疗方案,这是压倒性的临床医生为一个特定的病人做出最好的治疗。生物医学信息检索系统是缓解这一问题的有效途径之一,该系统可以从大量的替代治疗和病例中自动找到相关的信息和合适的治疗方法。然而,在生物医学文献和临床试验中,存在大量同义、多义和上下文术语,导致传统生物医学信息检索系统中查询与文献之间存在语义差距。近年来,基于深度学习的生物医学信息检索系统被用于解决这一问题,这有可能提高BMIR的性能。这些方法将查询和文档的语义信息编码为低维特征向量。现有的基于深度学习的生物医学信息检索系统虽然具有较强的准确性,但通常被视为缺乏可解释性的黑箱模型。临床医生很难理解他们的排名结果,这使他们怀疑这些系统的有效性。合理的解释有利于临床医生通过适当的治疗逻辑推理做出更好的决策,从而进一步提高生物医学信息检索系统的透明度、公平性和信任度。知识图谱包含了丰富的现实世界的事实和实体,越来越受到人们的重视。这是为深度学习模型提供准确性和可解释性,缩小专家与公众之间知识差距的有效途径。然而,在生物医学信息检索系统中,它通常只是作为一种查询扩展策略。如何将可解释的生物医学信息检索系统扩展到知识图谱,是一个有待解决的问题。鉴于此,为了缓解精准医疗的准确性和可解释性之间的权衡,我们提出了基于知识图谱的可解释精准医疗生物医学信息检索研究。在这项工作中,我们提出了一个基于神经的生物医学信息检索模型来解决语义缺口问题,并充分研究了KG在可解释生物医学信息检索系统中的应用。它可以用语义信息对查询和文档进行软匹配,而不是通过精确匹配对模型进行排序。一方面,我们的模型利用卷积神经网络对文档的语义特征信息进行编码,卷积神经网络近年来在文本信息建模方面表现出较强的能力。查询和文档之间的相关性将通过软匹配而不是精确匹配来衡量。另一方面,通过扩展知识图谱的实用性,赋予生物医学信息检索模型可解释性。基于图的策略可以通过在注意力分数的帮助下构建知识感知路径来实现这一目标。具体而言,将采用图注意网络(GAT)通过从图结构中总结高阶连通性来建模查询的表示。在gat级关注的帮助下,自动分配权重分数来构建知识感知的传播连通性,这可以作为进一步可解释的生物医学信息检索系统的证据。最后,该系统将通过TREC精密医学的数据集进行评估。
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引用次数: 15
Multi-Branch Convolutional Network for Context-Aware Recommendation 上下文感知推荐的多分支卷积网络
Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He
Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.
基于因子分解机(FM)的模型只能揭示一对特征之间的关系。当所有的特征嵌入都被输入到多层感知器(MLP)中,基于深度神经网络的FM与多层感知器(MLP)相结合的分解模型只能隐式地揭示部分特征之间的关系。其他一些基于dnn的方法应用CNN来生成特征交互。然而,(1)它们以位为单位(仅利用嵌入的一部分来生成特征交互)对特征交互建模,不能全面表达特征的语义;(2)它们只能对相邻特征之间的交互建模。为了解决上述问题,本文提出了一种多分支卷积网络(MBCN),该网络包括三个分支:标准卷积层、扩展卷积层和偏置层。MBCN能够在矢量方向上显式地对任意顺序的特征交互进行建模,从而充分表达上下文感知的特征语义。在三个公共基准数据集上进行了广泛的实验,以证明MBCN与上下文感知的top-k推荐的最先进基线相比的优越性。
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引用次数: 4
Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to Intervene Novel Coronavirus COVID-19 Outbreak 关注热点:新型冠状病毒病区预测预警系统介入新型冠状病毒疫情
Zhenxin Fu, Yuehua Wu, Hailei Zhang, Yichuan Hu, Dongyan Zhao, Rui Yan
Dating back from late December 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia, now known as lung inflammation caused by novel coronavirus (COVID-19). Cases have spread to other cities in China and more than 180 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency is perhaps one of the top concerns in the year of 2020 for governments all over the world. Till today, the coronavirus outbreak is still raging and has no sign of being under control in many countries. In this paper, we aim at drawing lessons from the COVID-19 outbreak process in China and using the experiences to help the interventions against the coronavirus wherever in need. To this end, we have built a system predicting hazard areas on the basis of confirmed infection cases with location information. The purpose is to warn people to avoid of such hot zones and reduce risks of disease transmission through droplets or contacts. We analyze the data from the daily official information release which are publicly accessible. Based on standard classification frameworks with reinforcements incrementally learned day after day, we manage to conduct thorough feature engineering from empirical studies, including geographical, demographic, temporal, statistical, and epidemiological features. Compared with heuristics baselines, our method has achieved promising overall performance in terms of precision, recall, accuracy, F1 score, and AUC. We expect that our efforts could be of help in the battle against the virus, the common opponent of human kind.
自2019年12月下旬以来,中国武汉市报告了一场非典型肺炎的爆发,现在被称为由新型冠状病毒(COVID-19)引起的肺部炎症。病例已蔓延到中国其他城市和国际180多个国家和地区。世界卫生组织(世卫组织)正式宣布冠状病毒爆发为大流行,公共卫生紧急情况可能是2020年世界各国政府最关心的问题之一。直到今天,冠状病毒疫情仍在肆虐,在许多国家没有得到控制的迹象。在本文中,我们旨在从中国的新冠肺炎疫情过程中吸取经验教训,并利用这些经验帮助在需要的地方采取干预措施。为此,我们建立了一个基于确诊病例和位置信息的危险区预测系统。目的是警告人们避开这些热区,减少通过飞沫或接触传播疾病的风险。我们分析的数据来自于每日公开发布的官方信息。基于标准分类框架和日复一日的增强学习,我们设法从经验研究中进行彻底的特征工程,包括地理、人口、时间、统计和流行病学特征。与启发式基线相比,我们的方法在准确率、召回率、准确率、F1分数和AUC方面取得了令人满意的总体性能。我们希望我们的努力能够对抗击病毒这一人类共同的敌人有所帮助。
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引用次数: 7
What If Bots Feel Moods? 如果机器人能感觉到情绪呢?
L. Qiu, Yingwai Shiu, Pingping Lin, Ruihua Song, Yue Liu, Dongyan Zhao, Rui Yan
For social bots, smooth emotional transitions are essential for delivering a genuine conversation experience to users. Yet, the task is challenging because emotion is too implicit and complicated to understand. Among previous studies in the domain of retrieval-based conversational model, they only consider the factors of semantic and functional dependencies of utterances. In this paper, to implement a more empathetic retrieval-based conversation system, we incorporate emotional factors into context-response matching from two aspects: 1) On top of semantic matching, we propose an emotion-aware transition network to model the dynamic emotional flow and enhance context-response matching in retrieval-based dialogue systems with learnt intrinsic emotion features through a multi-task learning framework; 2) We design several flexible controlling mechanisms to customize social bots in terms of emotion. Extensive experiments on two benchmark datasets indicate that the proposed model can effectively track the flow of emotions throughout a human-machine conversation and significantly improve response selection in dialogues over the state-of-the-art baselines. We also empirically validate the emotion-control effects of our proposed model on three different emotional aspects. Finally, we apply such functionalities to a real IoT application.
对于社交机器人来说,流畅的情感转换对于向用户提供真实的对话体验至关重要。然而,这项任务是具有挑战性的,因为情感太过含蓄和复杂,难以理解。在基于检索的会话模型领域中,以往的研究只考虑话语的语义依赖和功能依赖因素。为了实现一个基于共情检索的对话系统,我们从两个方面将情感因素纳入到上下文-反应匹配中:1)在语义匹配的基础上,我们提出了一个情感感知转换网络来模拟动态情绪流,并通过多任务学习框架增强基于检索的对话系统中习得的内在情感特征的上下文-反应匹配;2)我们设计了一些灵活的控制机制,从情感方面定制社交机器人。在两个基准数据集上进行的大量实验表明,所提出的模型可以有效地跟踪整个人机对话中的情绪流动,并在最先进的基线上显著改善对话中的响应选择。我们还在三个不同的情绪方面实证验证了我们提出的模型的情绪控制效果。最后,我们将这些功能应用于实际的物联网应用。
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引用次数: 14
Data Poisoning Attacks against Differentially Private Recommender Systems 针对差分私有推荐系统的数据中毒攻击
Soumya Wadhwa, Saurabh Agrawal, Harsh Chaudhari, Deepthi Sharma, Kannan Achan
Recommender systems based on collaborative filtering are highly vulnerable to data poisoning attacks, where a determined attacker injects fake users with false user-item feedback, with an objective to either corrupt the recommender system or promote/demote a target set of items. Recently, differential privacy was explored as a defense technique against data poisoning attacks in the typical machine learning setting. In this paper, we study the effectiveness of differential privacy against such attacks on matrix factorization based collaborative filtering systems. Concretely, we conduct extensive experiments for evaluating robustness to injection of malicious user profiles by simulating common types of shilling attacks on real-world data and comparing the predictions of typical matrix factorization with differentially private matrix factorization.
基于协同过滤的推荐系统非常容易受到数据中毒攻击,在这种攻击中,一个有决心的攻击者向虚假用户注入虚假的用户-商品反馈,目的是破坏推荐系统或提升/降级目标商品集。最近,在典型的机器学习环境中,差分隐私被作为一种防御数据中毒攻击的技术进行了探索。在本文中,我们研究了差分隐私对基于矩阵分解的协同过滤系统的这种攻击的有效性。具体来说,我们进行了大量的实验,通过模拟对真实数据的常见类型的先令攻击,并比较典型矩阵分解和差分私有矩阵分解的预测,来评估对恶意用户配置文件注入的鲁棒性。
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引用次数: 12
Octopus 章鱼
Zheng Liu, Jianxun Lian, Junhan Yang, Defu Lian, Xing Xie
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引用次数: 0
CrossBERT: A Triplet Neural Architecture for Ranking Entity Properties CrossBERT:一种用于实体属性排序的三重神经结构
Jarana Manotumruksa, Jeffrey Dalton, E. Meij, Emine Yilmaz
Task-based Virtual Personal Assistants (VPAs) such as the Google Assistant, Alexa, and Siri are increasingly being adopted for a wide variety of tasks. These tasks are grounded in real-world entities and actions (e.g., book a hotel, organise a conference, or requesting funds). In this work we tackle the task of automatically constructing actionable knowledge graphs in response to a user query in order to support a wider variety of increasingly complex assistant tasks. We frame this as an entity property ranking task given a user query with annotated properties. We propose a new method for property ranking, CrossBERT. CrossBERT builds on the Bidirectional Encoder Representations from Transformers (BERT) and creates a new triplet network structure on cross query-property pairs that is used to rank properties. We also study the impact of using external evidence for query entities from textual entity descriptions. We perform experiments on two standard benchmark collections, the NTCIR-13 Actionable Knowledge Graph Generation (AKGG) task and Entity Property Identification (EPI) task. The results demonstrate that CrossBERT significantly outperforms the best performing runs from AKGG and EPI, as well as previous state-of-the-art BERT-based models. In particular, CrossBERT significantly improves Recall and NDCG by approximately 2-12% over the BERT models across the two used datasets.
基于任务的虚拟个人助理(vpa),如谷歌助理、Alexa和Siri,越来越多地被用于各种各样的任务。这些任务基于现实世界的实体和行为(例如,预订酒店、组织会议或请求资金)。在这项工作中,我们解决了响应用户查询自动构建可操作知识图的任务,以支持更广泛、更复杂的辅助任务。我们将其定义为给定带有带注释属性的用户查询的实体属性排序任务。我们提出了一种新的属性排序方法,CrossBERT。CrossBERT建立在双向编码器表示的基础上,并在交叉查询-属性对上创建了一个新的三重网络结构,用于对属性进行排序。我们还研究了从文本实体描述中使用外部证据查询实体的影响。我们在两个标准基准集合上进行了实验,即ntir -13可操作知识图谱生成(AKGG)任务和实体属性识别(EPI)任务。结果表明,CrossBERT显著优于AKGG和EPI的最佳表现,以及以前最先进的基于bert的模型。特别是,CrossBERT在两个使用的数据集上比BERT模型显著提高了召回率和NDCG约2-12%。
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引用次数: 4
期刊
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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