The judging of relevance has been a subject of study in information retrieval for a long time, especially in the creation of relevance judgments for test collections. While the criteria by which assessors? judge relevance has been intensively studied, little work has investigated the process individual assessors go through to judge the relevance of a document. In this paper, we focus on the process by which relevance is judged, and in particular, the degree of effort a user must expend to judge relevance. By better understanding this effort in isolation, we may provide data which can be used to create better models of search. We present the results of an empirical evaluation of the effort users must exert to judge the relevance of document, investigating the effect of relevance level and document size. Results suggest that 'relevant' documents require more effort to judge when compared to highly relevant and not relevant documents, and that effort increases as document size increases.
{"title":"Is relevance hard work?: evaluating the effort of making relevant assessments","authors":"R. Villa, Martin Halvey","doi":"10.1145/2484028.2484150","DOIUrl":"https://doi.org/10.1145/2484028.2484150","url":null,"abstract":"The judging of relevance has been a subject of study in information retrieval for a long time, especially in the creation of relevance judgments for test collections. While the criteria by which assessors? judge relevance has been intensively studied, little work has investigated the process individual assessors go through to judge the relevance of a document. In this paper, we focus on the process by which relevance is judged, and in particular, the degree of effort a user must expend to judge relevance. By better understanding this effort in isolation, we may provide data which can be used to create better models of search. We present the results of an empirical evaluation of the effort users must exert to judge the relevance of document, investigating the effect of relevance level and document size. Results suggest that 'relevant' documents require more effort to judge when compared to highly relevant and not relevant documents, and that effort increases as document size increases.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"15 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":"127834146","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}
Fernando Ruiz-Rico, D. Tomás, J. González, María-Consuelo Rubio-Sánchez
This paper presents an application for medicinal plants prescription based on text classification techniques. The system receives as an input a free text describing the symptoms of a user, and retrieves a ranked list of medicinal plants related to those symptoms. In addition, a set of links to Wikipedia are also provided, enriching the information about every medicinal plant presented to the user. In order to improve the accessibility to the application, the input can be written in six different languages, adapting the results accordingly. The application interface can be accessed from different devices and platforms.
{"title":"A multilingual and multiplatform application for medicinal plants prescription from medical symptoms","authors":"Fernando Ruiz-Rico, D. Tomás, J. González, María-Consuelo Rubio-Sánchez","doi":"10.1145/2484028.2484201","DOIUrl":"https://doi.org/10.1145/2484028.2484201","url":null,"abstract":"This paper presents an application for medicinal plants prescription based on text classification techniques. The system receives as an input a free text describing the symptoms of a user, and retrieves a ranked list of medicinal plants related to those symptoms. In addition, a set of links to Wikipedia are also provided, enriching the information about every medicinal plant presented to the user. In order to improve the accessibility to the application, the input can be written in six different languages, adapting the results accordingly. The application interface can be accessed from different devices and platforms.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"10 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133203729","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}
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.
{"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}
Improving query understanding is crucial for providing the user with information that suits her needs. To this end, the retrieval system must be able to deal with several sources of knowledge from which it could infer a topical context. The use of external sources of information for improving document retrieval has been extensively studied. Improvements with either structured or large sets of data have been reported. However, in these studies resources are often used separately and rarely combined together. We experiment in this paper a method that discounts documents based on their weighted divergence from a set of external resources. We present an evaluation of the combination of four resources on two standard TREC test collections. Our proposed method significantly outperforms a state-of-the-art Mixture of Relevance Models on one test collection, while no significant differences are detected on the other one.
{"title":"Estimating topical context by diverging from external resources","authors":"Romain Deveaud, E. SanJuan, P. Bellot","doi":"10.1145/2484028.2484148","DOIUrl":"https://doi.org/10.1145/2484028.2484148","url":null,"abstract":"Improving query understanding is crucial for providing the user with information that suits her needs. To this end, the retrieval system must be able to deal with several sources of knowledge from which it could infer a topical context. The use of external sources of information for improving document retrieval has been extensively studied. Improvements with either structured or large sets of data have been reported. However, in these studies resources are often used separately and rarely combined together. We experiment in this paper a method that discounts documents based on their weighted divergence from a set of external resources. We present an evaluation of the combination of four resources on two standard TREC test collections. Our proposed method significantly outperforms a state-of-the-art Mixture of Relevance Models on one test collection, while no significant differences are detected on the other one.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"107 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":"132341309","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}
Youngchul Cha, Bin Bi, Chu-Cheng Hsieh, Junghoo Cho
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.
{"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}
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.
{"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}
Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma, Meihong Zheng, Jing Qian, Kuo Zhang
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.
{"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}
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.
{"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}
The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the recommendations based on their textual similarity to the user profiles. In order to improve the ranking of popular sights, we combined the initial ranking with rankings based on Google Search, popularity and categories. Finally, we performed filtering based on the temporal context. Overall, our system performed well above average and median, and outperformed the baseline - Google Places only -- run.
{"title":"Recommending personalized touristic sights using google places","authors":"Maya Sappelli, S. Verberne, Wessel Kraaij","doi":"10.1145/2484028.2484155","DOIUrl":"https://doi.org/10.1145/2484028.2484155","url":null,"abstract":"The purpose of the Contextual Suggestion track, an evaluation task at the TREC 2012 conference, is to suggest personalized tourist activities to an individual, given a certain location and time. In our content-based approach, we collected initial recommendations using the location context as search query in Google Places. We first ranked the recommendations based on their textual similarity to the user profiles. In order to improve the ranking of popular sights, we combined the initial ranking with rankings based on Google Search, popularity and categories. Finally, we performed filtering based on the temporal context. Overall, our system performed well above average and median, and outperformed the baseline - Google Places only -- run.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"72 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":"116182226","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}
Ryen W. White, E. Yom-Tov, E. Horvitz, Eugene Agichtein, W. Hersh
This workshop brings together researchers and practitioners from industry and academia to discuss search and discovery in the medi-cal domain. The event focuses on ways to make medical and health information more accessible to laypeople (including enhancements to ranking algorithms and search interfaces), and how we can dis-cover new medical facts and phenomena from information sought online, as evidenced in query streams and other sources such as social media. This domain also offers many opportunities for appli-cations that monitor and improve quality of life of those affected by medical conditions, by providing tools to support their health-related information behavior.
{"title":"Workshop on health search and discovery: helping users and advancing medicine","authors":"Ryen W. White, E. Yom-Tov, E. Horvitz, Eugene Agichtein, W. Hersh","doi":"10.1145/2484028.2484220","DOIUrl":"https://doi.org/10.1145/2484028.2484220","url":null,"abstract":"This workshop brings together researchers and practitioners from industry and academia to discuss search and discovery in the medi-cal domain. The event focuses on ways to make medical and health information more accessible to laypeople (including enhancements to ranking algorithms and search interfaces), and how we can dis-cover new medical facts and phenomena from information sought online, as evidenced in query streams and other sources such as social media. This domain also offers many opportunities for appli-cations that monitor and improve quality of life of those affected by medical conditions, by providing tools to support their health-related information behavior.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"7 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":"115149662","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}
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