The Experts below are selected from a list of 39489 Experts worldwide ranked by ideXlab platform
Sebastian Martschat - One of the best experts on this subject based on the ideXlab platform.
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multigraph Clustering for unsupervised coreference resolution
Meeting of the Association for Computational Linguistics, 2013Co-Authors: Sebastian MartschatAbstract:We present an unsupervised model for coreference resolution that casts the problem as a Clustering Task in a directed labeled weighted multigraph. The model outperforms most systems participating in the English track of the CoNLL’12 shared Task.
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ACL (Student Research Workshop) - Multigraph Clustering for Unsupervised Coreference Resolution
2013Co-Authors: Sebastian MartschatAbstract:We present an unsupervised model for coreference resolution that casts the problem as a Clustering Task in a directed labeled weighted multigraph. The model outperforms most systems participating in the English track of the CoNLL’12 shared Task.
Paolo Rosso - One of the best experts on this subject based on the ideXlab platform.
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a general bio inspired method to improve the short text Clustering Task
International Conference on Computational Linguistics, 2010Co-Authors: Diego Alejandro Ingaramo, Marcelo Luis Errecalde, Paolo RossoAbstract:“Short-text Clustering” is a very important research field due to the current tendency for people to use very short documents, e.g. blogs, text-messaging and others. In some recent works, new Clustering algorithms have been proposed to deal with this difficult problem and novel bio-inspired methods have reported the best results in this area. In this work, a general bio-inspired method based on the AntTree approach is proposed for this Task. It takes as input the results obtained by arbitrary Clustering algorithms and refines them in different stages. The proposal shows an interesting improvement in the results obtained with different algorithms on several short-text collections.
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CICLing - A general bio-inspired method to improve the short-text Clustering Task
Computational Linguistics and Intelligent Text Processing, 2010Co-Authors: Diego Alejandro Ingaramo, Marcelo Luis Errecalde, Paolo RossoAbstract:“Short-text Clustering” is a very important research field due to the current tendency for people to use very short documents, e.g. blogs, text-messaging and others. In some recent works, new Clustering algorithms have been proposed to deal with this difficult problem and novel bio-inspired methods have reported the best results in this area. In this work, a general bio-inspired method based on the AntTree approach is proposed for this Task. It takes as input the results obtained by arbitrary Clustering algorithms and refines them in different stages. The proposal shows an interesting improvement in the results obtained with different algorithms on several short-text collections.
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Defining and Evaluating Blog Characteristics
2009 Eighth Mexican International Conference on Artificial Intelligence, 2009Co-Authors: Fernando Perez Tellez, David Pinto, John Cardiff, Paolo RossoAbstract:The analysis of weblogs has become a popular area of natural language processing. Due to their specific characteristics, such as shortness, vocabulary size and nature,etc. it can be difficult to achieve good results using automated Clustering techniques. In particular, their nature can vary considerably, both in length and in breadth of topic. Without apriori knowledge of the nature of a blog it is difficult to achieve accurate Clustering results. In this paper, we present a framework for the assessment of a set of corpus features that will provide us with insight into their nature from a number of perspectives including shortness, broadness and class imbalance. This in turn allows us to assess the relative hardness of the Clustering Task and to identify components that can improve the accuracy of the Clustering Task. We furthermore present the results of some experiments in which we analyzed the features of two sample blog corpora, and we compared the results with other kinds of short texts.
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sense cluster based categorization and Clustering of abstracts
Lecture Notes in Computer Science, 2006Co-Authors: Davide Buscaldi, Paolo Rosso, Mikhail Alexandrov, Alfons Juan CiscarAbstract:This paper focuses on the use of sense clusters for classification and Clustering of very short texts such as conference abstracts. Common keyword-based techniques are effective for very short documents only when the data pertain to different domains. In the case of conference abstracts, all the documents are from a narrow domain (i.e., share a similar terminology), that increases the difficulty of the Task. Sense clusters are extracted from abstracts, exploiting the WordNet relationships existing between words in the same text. Experiments were carried out both for the categorization Task, using Bernoulli mixtures for binary data, and the Clustering Task, by means of Stein's MajorClust method.
D. Ienco - One of the best experts on this subject based on the ideXlab platform.
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Clustering view-segmented documents via tensor modeling
Lecture Notes in Computer Science, 2014Co-Authors: S. Romeo, A. Tagarelli, D. IencoAbstract:We propose a Clustering framework for view-segmented documents, i.e., relatively long documents made up of smaller fragments that can be provided according to a target set of views or aspects. The framework is designed to exploit a view-based document segmentation into a third-order tensor model, whose decomposition result would enable any standard document Clustering algorithm to better reflect the multi-faceted nature of the documents. Experimental results on document collections featuring paragraph-based, metadata-based, or user-driven views have shown the significance of the proposed approach, highlighting performance improvement in the document Clustering Task.
Peyman Neamatollahi - One of the best experts on this subject based on the ideXlab platform.
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Distributed Clustering-Task Scheduling for Wireless Sensor Networks Using Dynamic Hyper Round Policy
IEEE Transactions on Mobile Computing, 2018Co-Authors: Peyman Neamatollahi, Saeid Abrishami, Mahmoud Naghibzadeh, Mohammad Hossein YaghmaeeAbstract:Prolonging the network life cycle is an essential requirement for many types of Wireless Sensor Network (WSN) applications. Dynamic Clustering of sensors into groups is a popular strategy to maximize the network lifetime and increase scalability. In this strategy, to achieve the sensor nodes’ load balancing, with the aim of prolonging lifetime, network operations are split into rounds, i.e., fixed time intervals. Clusters are configured for the current round and reconfigured for the next round so that the costly role of the cluster head is rotated among the network nodes, i.e., Round-Based Policy (RBP). This load balancing approach potentially extends the network lifetime. However, the imposed overhead, due to the Clustering in every round, wastes network energy resources. This paper proposes a distributed energy-efficient scheme to cluster a WSN, i.e., Dynamic Hyper Round Policy (DHRP), which schedules Clustering-Task to extend the network lifetime and reduce energy consumption. Although DHRP is applicable to any data gathering protocols that value energy efficiency, a Simple Energy-efficient Data Collecting (SEDC) protocol is also presented to evaluate the usefulness of DHRP and calculate the end-to-end energy consumption. Experimental results demonstrate that SEDC with DHRP is more effective than two well-known Clustering protocols, HEED and M-LEACH, for prolonging the network lifetime and achieving energy conservation.
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Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Peyman Neamatollahi, Saeid Abrishami, Mahmoud Naghibzadeh, Mohammad Hossein Yaghmaee Moghaddam, O. YounisAbstract:Organizing sensor nodes into a clustered architecture is an effective method for load balancing and prolonging the network lifetime. However, a serious drawback of the Clustering approach is the imposed energy overhead caused by the “global” Clustering operations in every round of the global round-based policy (GRBP). To mitigate this problem, this paper proposes a hierarchical Clustering-Task scheduling policy (HCSP), which triggers node-driven Clustering as opposed to GRBP's time-driven Clustering. Based on HCSP, each cluster is reconfigured only once at each local super round. Therefore, the cluster reconfiguration frequency varies on-demand and may differ from one cluster to another throughout the network lifetime. However, in order to refresh the entire network structure, global Clustering is performed at the end of every global hyper round. Accordingly, HCSP aims to achieve a more flexible, energy-efficient, and scalable Clustering-Task scheduling than that of GRBP. This policy mitigates the Clustering overhead, which is the worst disadvantage of Clustering approaches. Energy consumption calculations and extensive simulations show the effectiveness of HCSP in saving energy and in prolonging the network lifetime.
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Fuzzy-Based Clustering-Task Scheduling for Lifetime Enhancement in Wireless Sensor Networks
IEEE Sensors Journal, 2017Co-Authors: Peyman Neamatollahi, Mahmoud Naghibzadeh, Saeid AbrishamiAbstract:Clustering is one of the effective approaches for prolonging the lifetime of a wireless sensor network and increasing its scalability. In current Clustering protocols, load balancing is achieved by rotating the costly role of the cluster head among the sensors. To achieve this, the network operation is divided into fixed time durations called rounds. Network nodes are clustered for one round and are reclustered for the next round, i.e., round-based policy. Using this policy, loads of nodes are somewhat balanced. However, the imposed overhead from consecutive reClusterings wastes the energy resource of network nodes. Although many attempts have been made to introduce energy-efficient Clustering protocols, the reClustering overhead still remains a serious drawback of these protocols. To mitigate this problem, this paper proposes a fuzzy-based hyper round policy (FHRP) to efficiently and flexibly schedule the Clustering-Task. In FHRP, instead of every round, Clustering is performed at the beginning of every Hyper Round (HR), which is composed of many rounds. During the network lifetime, the length of an HR is not fixed and is computed using a fuzzy inference system. The node’s residual energy and its distance from the sink are used as the inputs of this fuzzy system and the HR length is its output. Thus, the nodes’ situation is taken into account for determining the reClustering time. Simulation results reveal the effectiveness of FHRP in reducing the Clustering energy overhead, lengthening the network lifetime, and conserving the network nodes’ energy.
S. Romeo - One of the best experts on this subject based on the ideXlab platform.
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Clustering view-segmented documents via tensor modeling
Lecture Notes in Computer Science, 2014Co-Authors: S. Romeo, A. Tagarelli, D. IencoAbstract:We propose a Clustering framework for view-segmented documents, i.e., relatively long documents made up of smaller fragments that can be provided according to a target set of views or aspects. The framework is designed to exploit a view-based document segmentation into a third-order tensor model, whose decomposition result would enable any standard document Clustering algorithm to better reflect the multi-faceted nature of the documents. Experimental results on document collections featuring paragraph-based, metadata-based, or user-driven views have shown the significance of the proposed approach, highlighting performance improvement in the document Clustering Task.