Proceedings of the 21st ACM international conference on Information and knowledge management最新文献_第7页

Modeling topic hierarchies with the recursive chinese restaurant process 用递归中式餐厅流程建模主题层次结构

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396861

Joonyeob Kim, Dongwoo Kim, Suin Kim, Alice H. Oh

{"title":"Modeling topic hierarchies with the recursive chinese restaurant process","authors":"Joonyeob Kim, Dongwoo Kim, Suin Kim, Alice H. Oh","doi":"10.1145/2396761.2396861","DOIUrl":"https://doi.org/10.1145/2396761.2396861","url":null,"abstract":"Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the topics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using heldout likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized toward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"97 2 Suppl 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116374965","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: 53

Is wikipedia too difficult?: comparative analysis of readability of wikipedia, simple wikipedia and britannica 维基百科是不是太难了?:维基百科、简易维基百科和大英百科的可读性比较分析

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398703

A. Jatowt, Katsumi Tanaka

引用次数: 13

Shard ranking and cutoff estimation for topically partitioned collections 主题分区集合的分片排序和截止估计

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396833

Anagha Kulkarni, Almer S. Tigelaar, D. Hiemstra, Jamie Callan

{"title":"Shard ranking and cutoff estimation for topically partitioned collections","authors":"Anagha Kulkarni, Almer S. Tigelaar, D. Hiemstra, Jamie Callan","doi":"10.1145/2396761.2396833","DOIUrl":"https://doi.org/10.1145/2396761.2396833","url":null,"abstract":"Large document collections can be partitioned into 'topical shards' to facilitate distributed search. In a low-resource search environment only a few of the shards can be searched in parallel. Such a search environment faces two intertwined challenges. First, determining which shards to consult for a given query: shard ranking. Second, how many shards to consult from the ranking: cutoff estimation. In this paper we present a family of three algorithms that address both of these problems. As a basis we employ a commonly used data structure, the central sample index (CSI), to represent the shard contents. Running a query against the CSI yields a flat document ranking that each of our algorithms transforms into a tree structure. A bottom up traversal of the tree is used to infer a ranking of shards and also to estimate a stopping point in this ranking that yields cost-effective selective distributed search. As compared to a state-of-the-art shard ranking approach the proposed algorithms provide substantially higher search efficiency while providing comparable search effectiveness.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125746579","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: 46

LINDA: distributed web-of-data-scale entity matching 琳达:分布式数据网络规模的实体匹配

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398582

Christoph Böhm, Gerard de Melo, Felix Naumann, G. Weikum

引用次数: 94

One seed to find them all: mining opinion features via association 找到它们的一个种子:通过关联挖掘意见特征

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396797

Zhen Hai, Kuiyu Chang, G. Cong

{"title":"One seed to find them all: mining opinion features via association","authors":"Zhen Hai, Kuiyu Chang, G. Cong","doi":"10.1145/2396761.2396797","DOIUrl":"https://doi.org/10.1145/2396761.2396797","url":null,"abstract":"Feature-based opinion analysis has attracted extensive attention recently. Identifying features associated with opinions expressed in reviews is essential for fine-grained opinion mining. One approach is to exploit the dependency relations that occur naturally between features and opinion words, and among features (or opinion words) themselves. In this paper, we propose a generalized approach to opinion feature extraction by incorporating robust statistical association analysis in a bootstrapping framework. The new approach starts with a small set of feature seeds, on which it iteratively enlarges by mining feature-opinion, feature-feature, and opinion-opinion dependency relations. Two association model types, namely likelihood ratio tests (LRT) and latent semantic analysis (LSA), are proposed for computing the pair-wise associations between terms (features or opinions). We accordingly propose two robust bootstrapping approaches, LRTBOOT and LSABOOT, both of which need just a handful of initial feature seeds to bootstrap opinion feature extraction. We benchmarked LRTBOOT and LSABOOT against existing approaches on a large number of real-life reviews crawled from the cellphone and hotel domains. Experimental results using varying number of feature seeds show that the proposed association-based bootstrapping approach significantly outperforms the competitors. In fact, one seed feature is all that is needed for LRTBOOT to significantly outperform the other methods. This seed feature can simply be the domain feature, e.g., \"cellphone\" or \"hotel\". The consequence of our discovery is far reaching: starting with just one feature seed, typically just the domain concept word, LRTBOOT can automatically extract a large set of high-quality opinion features from the corpus without any supervision or labeled features. This means that the automatic creation of a set of domain features is no longer a pipe dream!","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128700641","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: 71

Estimating query difficulty for news prediction retrieval 新闻预测检索查询难度估计

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398707

Nattiya Kanhabua, K. Nørvåg

引用次数: 0

Clustering Wikipedia infoboxes to discover their types 聚集维基百科信息框以发现它们的类型

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398588

T. Nguyen, Huong Nguyen, V. Moreira, J. Freire

引用次数: 1

Diversionary comments under political blog posts 政治博客帖子下的转移注意力的评论

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398518

Jing Wang, Clement T. Yu, Philip S. Yu, B. Liu, W. Meng

引用次数: 14

Data filtering in humor generation: comparative analysis of hit rate and co-occurrence rankings as a method to choose usable pun candidates 幽默生成中的数据过滤:对比分析命中率和共现排名，选择可用的双关语候选词

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398698

Pawel Dybala, Rafal Rzepka, K. Araki, Kohichi Sayama

{"title":"Data filtering in humor generation: comparative analysis of hit rate and co-occurrence rankings as a method to choose usable pun candidates","authors":"Pawel Dybala, Rafal Rzepka, K. Araki, Kohichi Sayama","doi":"10.1145/2396761.2398698","DOIUrl":"https://doi.org/10.1145/2396761.2398698","url":null,"abstract":"In this paper we propose a method of filtering excessive amount of textual data acquired from the Internet. In our research on pun generation in Japanese we experienced problems with extensively long data processing time, caused by the amount of phonetic candidates generated (i.e. phrases that can be used to generate actual puns) by our system. Simple, naive approach in which we take into considerations only phrases with the highest occurrence in the Internet, can effect in deletion of those candidates that are actually usable. Thus, we propose a data filtering method in which we compare two Internet-based rankings: a co-occurrence ranking and a hit rate ranking, and select only candidates which occupy the same or similar positions in these rankings. In this work we analyze the effects of such data reduction, considering 1 cases: when the candidates are on exactly the same positions in both rankings, and when their positions differ by 1, 2, 3 and 4. The analysis is conducted on data acquired by comparing pun candidates generated by the system (and filtered with our method) with phrases that were actually used in puns created by humans. The results show that the proposed method can be used to filter excessive amounts of textual data acquired from the Internet.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126938242","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: 1

SonetRank: leveraging social networks to personalize search sononerank:利用社交网络进行个性化搜索

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398569

Abhijith Kashyap, R. Amini, Vagelis Hristidis

{"title":"SonetRank: leveraging social networks to personalize search","authors":"Abhijith Kashyap, R. Amini, Vagelis Hristidis","doi":"10.1145/2396761.2398569","DOIUrl":"https://doi.org/10.1145/2396761.2398569","url":null,"abstract":"Earlier works on personalized Web search focused on the click-through graphs, while recent works leverage social annotations, which are often unavailable. On the other hand, many users are members of the social networks and subscribe to social groups. Intuitively, users in the same group may have similar relevance judgments for queries related to these groups. SonetRank utilizes this observation to personalize the Web search results based on the aggregate relevance feedback of the users in similar groups. SonetRank builds and maintains a rich graph-based model, termed Social Aware Search Graph, consisting of groups, users, queries and results click-through information. SonetRank's personalization scheme learns in a principled way to leverage the following three signals, of decreasing strength: the personal document preferences of the user, of the users of her social groups relevant to the query, and of the other users in the network. SonetRank also uses a novel approach to measure the amount of personalization with respect to a user and a query, based on the query-specific richness of the user's social profile. We evaluate SonetRank with users on Amazon Mechanical Turk and show a significant improvement in ranking compared to state-of-the-art techniques.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"3 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130523218","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: 18