Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第7页

Session details: Session 2C: Knowledge and Entities 会议详情:会议2C:知识和实体

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3349680

A. D. Vries

引用次数: 0

A Systematic Comparison of Methods for Finding Good Premises for Claims 寻找良好索赔前提方法的系统比较

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331282

Lorik Dumani, Ralf Schenkel

引用次数: 15

PSGAN

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331218

Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, Ji-rong Wen

{"title":"PSGAN","authors":"Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, Ji-rong Wen","doi":"10.1145/3331184.3331218","DOIUrl":"https://doi.org/10.1145/3331184.3331218","url":null,"abstract":"Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84767256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38

Multi-Level Matching Networks for Text Matching 用于文本匹配的多级匹配网络

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331276

Chunlin Xu, Zhiwei Lin, Shengli Wu, Hui Wang

{"title":"Multi-Level Matching Networks for Text Matching","authors":"Chunlin Xu, Zhiwei Lin, Shengli Wu, Hui Wang","doi":"10.1145/3331184.3331276","DOIUrl":"https://doi.org/10.1145/3331184.3331276","url":null,"abstract":"Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answering, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of-the-art performance in text matching. A major limitation of existing works is that only high level contextualized word representations are utilized to obtain word level matching results without considering other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different meanings are very close in high level contextualized word representation space. Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple levels of word representations to obtain multiple word level matching results for final text level matching decision. Experimental results on two widely used benchmarks, SNLI and Scaitail, show that the proposed MMN achieves the state-of-the-art performance.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86432677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Health Cards for Consumer Health Search 用于消费者健康检索的健康卡

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331194

Jimmy, G. Zuccon, B. Koopman, Gianluca Demartini

{"title":"Health Cards for Consumer Health Search","authors":"Jimmy, G. Zuccon, B. Koopman, Gianluca Demartini","doi":"10.1145/3331184.3331194","DOIUrl":"https://doi.org/10.1145/3331184.3331194","url":null,"abstract":"This paper investigates the impact of health cards in consumer health search (CHS) - people seeking health advice online. Health cards are a concise presentations of a health concept shown along side search results to specific health queries; they have the potential to convey health information in easily digestible form for the general public. However, little evidence exists on how effective health cards actually are for users when searching health advice online, and whether their effectiveness is limited to specific health search intents. To understand the impact of health cards on CHS, we conducted a laboratory study to observe users completing CHS tasks using two search interface variants: one just with result snippets and one containing both result snippets and health cards. Our study makes the following contributions: (1) it reveals how and when health cards are beneficial to users in completing consumer health search tasks, and (2) it identifies the features of health cards that helped users in completing their tasks. This is the first study that thoroughly investigates the effectiveness of health cards in supporting consumer health search.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"160 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86445645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Scalable Deep Multimodal Learning for Cross-Modal Retrieval 跨模态检索的可扩展深度多模态学习

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331213

Peng Hu, Liangli Zhen, Dezhong Peng, Pei Liu

{"title":"Scalable Deep Multimodal Learning for Cross-Modal Retrieval","authors":"Peng Hu, Liangli Zhen, Dezhong Peng, Pei Liu","doi":"10.1145/3331184.3331213","DOIUrl":"https://doi.org/10.1145/3331184.3331213","url":null,"abstract":"Cross-modal retrieval takes one type of data as the query to retrieve relevant data of another type. Most of existing cross-modal retrieval approaches were proposed to learn a common subspace in a joint manner, where the data from all modalities have to be involved during the whole training process. For these approaches, the optimal parameters of different modality-specific transformations are dependent on each other and the whole model has to be retrained when handling samples from new modalities. In this paper, we present a novel cross-modal retrieval method, called Scalable Deep Multimodal Learning (SDML). It proposes to predefine a common subspace, in which the between-class variation is maximized while the within-class variation is minimized. Then, it trains m modality-specific networks for m modalities (one network for each modality) to transform the multimodal data into the predefined common subspace to achieve multimodal learning. Unlike many of the existing methods, our method can train different modality-specific networks independently and thus be scalable to the number of modalities. To the best of our knowledge, the proposed SDML could be one of the first works to independently project data of an unfixed number of modalities into a predefined common subspace. Comprehensive experimental results on four widely-used benchmark datasets demonstrate that the proposed method is effective and efficient in multimodal learning and outperforms the state-of-the-art methods in cross-modal retrieval.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86340371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 76

Investigating Passage-level Relevance and Its Role in Document-level Relevance Judgment 文章级关联及其在文件级关联判断中的作用研究

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331233

Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

{"title":"Investigating Passage-level Relevance and Its Role in Document-level Relevance Judgment","authors":"Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma","doi":"10.1145/3331184.3331233","DOIUrl":"https://doi.org/10.1145/3331184.3331233","url":null,"abstract":"The understanding of the process of relevance judgment helps to inspire the design of retrieval models. Traditional retrieval models usually estimate relevance based on document-level signals. Recent works consider a more fine-grain, passage-level relevance information, which can further enhance retrieval performance. However, it lacks a detailed analysis of how passage-level relevance signals determine or influence the relevance judgment of the whole document. To investigate the role of passage-level relevance in the document-level relevance judgment, we construct an ad-hoc retrieval dataset with both passage-level and document-level relevance labels. A thorough analysis reveals that: 1) there is a strong correlation between the document-level relevance and the fractions of irrelevant passages to highly relevant passages; 2) the position, length and query similarity of passages play different roles in the determination of document-level relevance; 3) The sequential passage-level relevance within a document is a potential indicator for the document-level relevance. Based on the relationship between passage-level and document-level relevance, we also show that utilizing passage-level relevance signals can improve existing document ranking models. This study helps us better understand how users perceive relevance for a document and inspire the designing of novel ranking models leveraging fine-grain, passage-level relevance signals.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86731504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Query-Task Mapping Query-Task映射

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331286

Michael Völske, Ehsan Fatehifar, Benno Stein, Matthias Hagen

引用次数: 7

Session details: Session 7A: Relevance and Evaluation 1 会议详情:会议7A:相关性和评估

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3349691

M. Sanderson

引用次数: 0

Improving Collaborative Metric Learning with Efficient Negative Sampling 利用高效负抽样改进协同度量学习

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI: 10.1145/3331184.3331337

Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier, Manuel Moussallam

引用次数: 16