Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval最新文献_第5页

Is relevance hard work?: evaluating the effort of making relevant assessments 相关性很难吗?:对相关评估工作的评价

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484150

R. Villa, Martin Halvey

引用次数: 36

A multilingual and multiplatform application for medicinal plants prescription from medical symptoms 一个多语言、多平台的药用植物处方应用程序

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484201

Fernando Ruiz-Rico, D. Tomás, J. González, María-Consuelo Rubio-Sánchez

引用次数: 0

Interpretation of coordinations, compound generation, and result fusion for query variants 解释查询变量的协调、复合生成和结果融合

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484115

Johannes Leveling

{"title":"Interpretation of coordinations, compound generation, and result fusion for query variants","authors":"Johannes Leveling","doi":"10.1145/2484028.2484115","DOIUrl":"https://doi.org/10.1145/2484028.2484115","url":null,"abstract":"We investigate interpreting coordinations (e.g. word sequences connected with coordinating conjunctions such as \"and\" and \"or\") as logical disjunctions of terms to generate a set of disjunctionfree query variants for information retrieval (IR) queries. In addition, so-called hyphen coordinations are resolved by generating full compound forms and rephrasing the original query, e.g. \"rice im-and export\" is transformed into \"rice import and export\". Query variants are then processed separately and retrieval results are merged using a standard data fusion technique. We evaluate the approach on German standard IR benchmarking data. The results show that: i) Our proposed approach to generate compounds from hyphen coordinations produces the correct results for all test topics. ii) Our proposed heuristics to identify coordinations and generate query variants based on shallow natural language processing (NLP) techniques is highly accurate on the topics and does not rely on parsing or part-of-speech tagging. iii) Using query variants to produce multiple retrieval results and merging the results decreases precision at top ranks. However, in combination with blind relevance feedback (BRF), this approach can show significant improvement over the standard BRF baseline using the original queries.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133226472","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: 4

Estimating topical context by diverging from external resources 通过偏离外部资源来估计主题背景

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484148

Romain Deveaud, E. SanJuan, P. Bellot

引用次数: 7

Incorporating popularity in topic models for social network analysis 将流行度纳入社会网络分析的主题模型

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484086

Youngchul Cha, Bin Bi, Chu-Cheng Hsieh, Junghoo Cho

{"title":"Incorporating popularity in topic models for social network analysis","authors":"Youngchul Cha, Bin Bi, Chu-Cheng Hsieh, Junghoo Cho","doi":"10.1145/2484028.2484086","DOIUrl":"https://doi.org/10.1145/2484028.2484086","url":null,"abstract":"Topic models are used to group words in a text dataset into a set of relevant topics. Unfortunately, when a few words frequently appear in a dataset, the topic groups identified by topic models become noisy because these frequent words repeatedly appear in \"irrelevant\" topic groups. This noise has not been a serious problem in a text dataset because the frequent words (e.g., the and is) do not have much meaning and have been simply removed before a topic model analysis. However, in a social network dataset we are interested in, they correspond to popular persons (e.g., Barack Obama and Justin Bieber) and cannot be simply removed because most people are interested in them. To solve this \"popularity problem\", we explicitly model the popularity of nodes (words) in topic models. For this purpose, we first introduce a notion of a \"popularity component\" and propose topic model extensions that effectively accommodate the popularity component. We evaluate the effectiveness of our models with a real-world Twitter dataset. Our proposed models achieve significantly lower perplexity (i.e., better prediction power) compared to the state-of-the-art baselines. In addition to the popularity problem caused by the nodes with high incoming edge degree, we also investigate the effect of the outgoing edge degree with another topic model extensions. We show that considering outgoing edge degree does not help much in achieving lower perplexity.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134438144","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: 41

Exploiting hybrid contexts for Tweet segmentation 利用混合上下文进行Tweet分割

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484044

Chenliang Li, Aixin Sun, J. Weng, Qi He

{"title":"Exploiting hybrid contexts for Tweet segmentation","authors":"Chenliang Li, Aixin Sun, J. Weng, Qi He","doi":"10.1145/2484028.2484044","DOIUrl":"https://doi.org/10.1145/2484028.2484044","url":null,"abstract":"Twitter has attracted hundred millions of users to share and disseminate most up-to-date information. However, the noisy and short nature of tweets makes many applications in information retrieval (IR) and natural language processing (NLP) challenging. Recently, segment-based tweet representation has demonstrated effectiveness in named entity recognition (NER) and event detection from tweet streams. To split tweets into meaningful phrases or segments, the previous work is purely based on external knowledge bases, which ignores the rich local context information embedded in the tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. HybridSeg incorporates local context knowledge with global knowledge bases for better tweet segmentation. HybridSeg consists of two steps: learning from off-the-shelf weak NERs and learning from pseudo feedback. In the first step, the existing NER tools are applied to a batch of tweets. The named entities recognized by these NERs are then employed to guide the tweet segmentation process. In the second step, HybridSeg adjusts the tweet segmentation results iteratively by exploiting all segments in the batch of tweets in a collective manner. Experiments on two tweet datasets show that HybridSeg significantly improves tweet segmentation quality compared with the state-of-the-art algorithm. We also conduct a case study by using tweet segments for the task of named entity recognition from tweets. The experimental results demonstrate that HybridSeg significantly benefits the downstream applications.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130339808","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: 34

Incorporating vertical results into search click models 将垂直结果整合到搜索点击模型中

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484036

Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Meihong Zheng, Jing Qian, Kuo Zhang

{"title":"Incorporating vertical results into search click models","authors":"Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Meihong Zheng, Jing Qian, Kuo Zhang","doi":"10.1145/2484028.2484036","DOIUrl":"https://doi.org/10.1145/2484028.2484036","url":null,"abstract":"In modern search engines, an increasing number of search result pages (SERPs) are federated from multiple specialized search engines (called verticals, such as Image or Video). As an effective approach to interpret users' click-through behavior as feedback information, most click models were designed to reduce the position bias and improve ranking performance of ordinary search results, which have homogeneous appearances. However, when vertical results are combined with ordinary ones, significant differences in presentation may lead to user behavior biases and thus failure of state-of-the-art click models. With the help of a popular commercial search engine in China, we collected a large scale log data set which contains behavior information on both vertical and ordinary results. We also performed eye-tracking analysis to study user's real-world examining behavior. According these analysis, we found that different result appearances may cause different behavior biases both for vertical results (local effect) and for the whole result lists (global effect). These biases include: examine bias for vertical results (especially those with multimedia components), trust bias for result lists with vertical results, and a higher probability of result revisitation for vertical results. Based on these findings, a novel click model considering these biases besides position bias was constructed to describe interaction with SERPs containing verticals. Experimental results show that the new Vertical-aware Click Model (VCM) is better at interpreting user click behavior on federated searches in terms of both log-likelihood and perplexity than existing models.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114794224","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: 97

How query cost affects search behavior 查询成本如何影响搜索行为

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484049

L. Azzopardi, D. Kelly, Kathy Brennan

{"title":"How query cost affects search behavior","authors":"L. Azzopardi, D. Kelly, Kathy Brennan","doi":"10.1145/2484028.2484049","DOIUrl":"https://doi.org/10.1145/2484028.2484049","url":null,"abstract":"affects how users interact with a search system. Microeconomic theory is used to generate the cost-interaction hypothesis that states as the cost of querying increases, users will pose fewer queries and examine more documents per query. A between-subjects laboratory study with 36 undergraduate subjects was conducted, where subjects were randomly assigned to use one of three search interfaces that varied according to the amount of physical cost required to query: Structured (high cost), Standard (medium cost) and Query Suggestion (low cost). Results show that subjects who used the Structured interface submitted significantly fewer queries, spent more time on search results pages, examined significantly more documents per query, and went to greater depths in the search results list. Results also showed that these subjects spent longer generating their initial queries, saved more relevant documents and rated their queries as more successful. These findings have implications for the usefulness of microeconomic theory as a way to model and explain search interaction, as well as for the design of query facilities.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117122337","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: 88

Recommending personalized touristic sights using google places 使用谷歌位置推荐个性化的旅游景点

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484155

Maya Sappelli, S. Verberne, Wessel Kraaij

引用次数: 13

Workshop on health search and discovery: helping users and advancing medicine 健康搜索和发现:帮助用户和推进医学研讨会

Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2013-07-28 DOI: 10.1145/2484028.2484220

Ryen W. White, E. Yom-Tov, E. Horvitz, Eugene Agichtein, W. Hersh

引用次数: 2