Proceedings of the 25th ACM International on Conference on Information and Knowledge Management最新文献_第5页

When is the Time Ripe for Natural Language Processing for Patent Passage Retrieval? 自然语言处理专利文献检索的时机何时成熟?

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983858

Linda Andersson, M. Lupu, João Palotti, A. Hanbury, A. Rauber

{"title":"When is the Time Ripe for Natural Language Processing for Patent Passage Retrieval?","authors":"Linda Andersson, M. Lupu, João Palotti, A. Hanbury, A. Rauber","doi":"10.1145/2983323.2983858","DOIUrl":"https://doi.org/10.1145/2983323.2983858","url":null,"abstract":"Patent text is a mixture of legal terms and domain specific terms. In technical English text, a multi-word unit method is often deployed as a word formation strategy in order to expand the working vocabulary, i.e. introducing a new concept without the invention of an entirely new word. In this paper we explore query generation using natural language processing technologies in order to capture domain specific concepts represented as multi-word units. In this paper we examine a range of query generation methods using both linguistic and statistical information. We also propose a new method to identify domain specific terms from other more general phrases. We apply a machine learning approach using domain knowledge and corpus linguistic information in order to learn domain specific terms in relation to phrases' Termhood values. The experiments are conducted on the English part of the CLEF-IP 2013 test collection. The outcome of the experiments shows that the favoured method in terms of PRES and recall is when a language model is used and search terms are extracted with a part-of-speech tagger and a noun phrase chunker. With our proposed methods we improve each evaluation metric significantly compared to the existing state-of-the-art for the CLEP-IP 2013 test collection: for PRES@100 by 26% (0.544 from 0.433), for recall@100 by 17% (0.631 from 0.540) and on document MAP by 57% (0.300 from 0.191).","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124590043","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: 16

Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation Tag2Word:使用标签为基于内容的标签推荐生成单词

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983682

Yong Wu, Yuan Yao, F. Xu, Hanghang Tong, Jian Lu

引用次数: 35

City-Scale Localization with Telco Big Data 利用电信大数据实现城市规模的本地化

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983345

Fangzhou Zhu, Chen Luo, Mingxuan Yuan, Yijian Zhu, Zhengqing Zhang, Tao Gu, Ke Deng, Weixiong Rao, Jia Zeng

{"title":"City-Scale Localization with Telco Big Data","authors":"Fangzhou Zhu, Chen Luo, Mingxuan Yuan, Yijian Zhu, Zhengqing Zhang, Tao Gu, Ke Deng, Weixiong Rao, Jia Zeng","doi":"10.1145/2983323.2983345","DOIUrl":"https://doi.org/10.1145/2983323.2983345","url":null,"abstract":"It is still challenging in telecommunication (telco) industry to accurately locate mobile devices (MDs) at city-scale using the measurement report (MR) data, which measure parameters of radio signal strengths when MDs connect with base stations (BSs) in telco networks for making/receiving calls or mobile broadband (MBB) services. In this paper, we find that the widely-used location based services (LBSs) have accumulated lots of over-the-top (OTT) global positioning system (GPS) data in telco networks, which can be automatically used as training labels for learning accurate MR-based positioning systems. Benefiting from these telco big data, we deploy a context-aware coarse-to-fine regression (CCR) model in Spark/Hadoop-based telco big data platform for city-scale localization of MDs with two novel contributions. First, we design map-matching and interpolation algorithms to encode contextual information of road networks. Second, we build a two-layer regression model to capture coarse-to-fine contextual features in a short time window for improved localization performance. In our experiments, we collect 108 GPS-associated MR records in the centroid of Shanghai city with 12 x 11 square kilometers for 30 days, and measure four important properties of real-world MR data related to localization errors: stability, sensitivity, uncertainty and missing values. The proposed CCR works well under different properties of MR data and achieves a mean error of 110m and a median error of $80m$, outperforming the state-of-art range-based and fingerprinting localization methods.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064259","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: 35

Cutty 极短的

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983807

Paris Carbone, Jonas Traub, Asterios Katsifodimos, Seif Haridi, Volker Markl

{"title":"Cutty","authors":"Paris Carbone, Jonas Traub, Asterios Katsifodimos, Seif Haridi, Volker Markl","doi":"10.1145/2983323.2983807","DOIUrl":"https://doi.org/10.1145/2983323.2983807","url":null,"abstract":"Aggregation queries on data streams are evaluated over evolving and often overlapping logical views called windows. While the aggregation of periodic windows were extensively studied in the past through the use of aggregate sharing techniques such as Panes and Pairs, little to no work has been put in optimizing the aggregation of very common, non-periodic windows. Typical examples of non-periodic windows are punctuations and sessions which can implement complex business logic and are often expressed as user-defined operators on platforms such as Google Dataflow or Apache Storm. The aggregation of such non-periodic or user-defined windows either falls back to expensive, best-effort aggregate sharing methods, or is not optimized at all. In this paper we present a technique to perform efficient aggregate sharing for data stream windows, which are declared as user-defined functions (UDFs) and can contain arbitrary business logic. To this end, we first introduce the concept of User-Defined Windows (UDWs), a simple, UDF-based programming abstraction that allows users to programmatically define custom windows. We then define semantics for UDWs, based on which we design Cutty, a low-cost aggregate sharing technique. Cutty improves and outperforms the state of the art for aggregate sharing on single and multiple queries. Moreover, it enables aggregate sharing for a broad class of non-periodic UDWs. We implemented our techniques on Apache Flink, an open source stream processing system, and performed experiments demonstrating orders of magnitude of reduction in aggregation costs compared to the state of the art.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"75 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116797248","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: 61

Survival Analysis based Framework for Early Prediction of Student Dropouts 基于生存分析的学生辍学早期预测框架

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983351

Sattar Ameri, M. J. Fard, R. Chinnam, C. Reddy

{"title":"Survival Analysis based Framework for Early Prediction of Student Dropouts","authors":"Sattar Ameri, M. J. Fard, R. Chinnam, C. Reddy","doi":"10.1145/2983323.2983351","DOIUrl":"https://doi.org/10.1145/2983323.2983351","url":null,"abstract":"Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of {emph{``at-risk''}} students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"662 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120877899","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: 86

Session details: Tutorials 会议详情:教程

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/3257193

Hui Fang, L. Khan, Charles K. Nicholas

引用次数: 0

Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression 基于矩阵分解回归的Twitter流中的近实时地理位置预测

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983887

Nghia Duong-Trung, Nicolas Schilling, L. Schmidt-Thieme

{"title":"Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression","authors":"Nghia Duong-Trung, Nicolas Schilling, L. Schmidt-Thieme","doi":"10.1145/2983323.2983887","DOIUrl":"https://doi.org/10.1145/2983323.2983887","url":null,"abstract":"Previous research on content-based geolocation in general has developed prediction methods via conducting pre-partitioning and applying classification methods. The input of these methods is the concatenation of individual tweets during a period of time. But unfortunately, these methods have some drawbacks. They discard the natural real-values properties of latitude and longitude as well as fail to capture geolocation in near real-time. In this work, we develop a novel generative content-based regression model via a matrix factorization technique to tackle the near real-time geolocation prediction problem. With this model, we aim to address a couple of un-answered questions. First, we prove that near real-time geolocation prediction can be accomplished if we leave out the concatenation. Second, we account the real-values properties of physical coordinates within a regression solution. We apply our model on Twitter datasets as an example to prove the effectiveness and generality. Our experimental results show that the proposed model, in the best scenario, outperforms a set of state-of-the-art regression models including Support Vector Machines and Factorization Machines by a reduction of the median localization error up to 79%.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126020270","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: 26

Incremental Mining of High Utility Sequential Patterns in Incremental Databases 增量数据库中高效用序列模式的增量挖掘

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983691

Jun-Zhe Wang, Jiun-Long Huang

{"title":"Incremental Mining of High Utility Sequential Patterns in Incremental Databases","authors":"Jun-Zhe Wang, Jiun-Long Huang","doi":"10.1145/2983323.2983691","DOIUrl":"https://doi.org/10.1145/2983323.2983691","url":null,"abstract":"High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner algorithm to mine HUSPs incrementally. Specifically, to avoid redundant computations, we propose a tighter upper bound of the utility of a sequence, called TSU, and then design a novel data structure, called the candidate pattern tree, to maintain the sequences whose TSU values are greater than or equal to the minimum utility threshold. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of auxiliary utility information is designed to be stored in each tree node. Moreover, for those nodes whose utilities have to be updated, a strategy is also proposed to reduce the amount of computation, thereby improving the mining efficiency. Experimental results on three real datasets show that IncUSP-Miner is able to efficiently mine HUSPs incrementally.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126149527","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

Topological Graph Sketching for Incremental and Scalable Analytics 用于增量和可扩展分析的拓扑图草图

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983735

Bortik Bandyopadhyay, David Fuhry, Aniket Chakrabarti, S. Parthasarathy

{"title":"Topological Graph Sketching for Incremental and Scalable Analytics","authors":"Bortik Bandyopadhyay, David Fuhry, Aniket Chakrabarti, S. Parthasarathy","doi":"10.1145/2983323.2983735","DOIUrl":"https://doi.org/10.1145/2983323.2983735","url":null,"abstract":"We propose a novel, scalable, and principled graph sketching technique based on minwise hashing of local neighborhood. For an n-node graph with e-edges (e >> n), we incrementally maintain in real-time a minwise neighbor sampled subgraph using k hash functions in O(n x k) memory, limit being user-configurable by the parameter k. Symmetrization and similarity based techniques can recover from these data structures a significant portion of the original graph. We present theoretical analysis of the minwise sampling strategy and also derive unbiased estimators for important graph properties such as triangle count and neighborhood overlap. We perform an extensive empirical evaluation of our graph sketch and it's derivatives on a wide variety of real-world graph data sets drawn from different application domains using important large network analysis algorithms: local and global clustering coefficient, PageRank, and local graph sparsification. With bounded memory, the quality of results using the sketch representation is competitive against baselines which use the full graph, and the computational performance is often better. Our framework is flexible and configurable to be leveraged by numerous other graph analytics algorithms, potentially reducing the information mining time on large streamed graphs for a variety of applications.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043017","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: 10

Estimating Time Models for News Article Excerpts 估计新闻文章摘录的时间模型

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI: 10.1145/2983323.2983802

Arunav Mishra, K. Berberich

{"title":"Estimating Time Models for News Article Excerpts","authors":"Arunav Mishra, K. Berberich","doi":"10.1145/2983323.2983802","DOIUrl":"https://doi.org/10.1145/2983323.2983802","url":null,"abstract":"It is often difficult to ground text to precise time intervals due to the inherent uncertainty arising from either missing or multiple expressions at year, month, and day time granularities. We address the problem of estimating an excerpt-time model capturing the temporal scope of a given news article excerpt as a probability distribution over chronons. For this, we propose a semi-supervised distribution propagation framework that leverages redundancy in the data to improve the quality of estimated time models. Our method generates an event graph with excerpts as nodes and models various inter-excerpt relations as edges. It then propagates empirical excerpt-time models estimated for temporally annotated excerpts, to those that are strongly related but miss annotations. In our experiments, we first generate a test query set by randomly sampling 100 Wikipedia events as queries. For each query, making use of a standard text retrieval model, we then obtain top-10 documents with an average of 150 excerpts. From these, each temporally annotated excerpt is considered as gold standard. The evaluation measures are first computed for each gold standard excerpt for a single query, by comparing the estimated model with our method to the empirical model from the original expressions. Final scores are reported by averaging over all the test queries. Experiments on the English Gigaword corpus show that our method estimates significantly better time models than several baselines taken from the literature.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127048448","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: 3