2,286 research outputs found
Twitter anticipates bursts of requests for Wikipedia articles
No full textMost of the tweets that users exchange on Twitter make implicit mentions of named-entities, which in turn can be mapped to corresponding Wikipedia articles using proper Entity Linking (EL) techniques. Some of those become trending entities on Twitter due to a long-lasting or a sudden effect on the volume of tweets where they are mentioned. We argue that the set of trending entities discovered from Twitter may help predict the volume of requests for relating Wikipedia articles. To validate this claim, we apply an EL technique to extract trending entities from a large dataset of public tweets. Then, we analyze the time series derived from the hourly trending score (i.e., an index of popularity) of each entity as measured by Twitter and Wikipedia, respectively. Our results reveals that Twitter actually leads Wikipedia by one or more hours
High performance: closed frequent itemsets mining inspired by emerging computer architectures
No full textSocial–Spatiotemporal Analysis of Topical and Polarized Communities in Online Social Networks
No full textEfficiency/Effectiveness Trade-offs in Learning to Rank
No full textIn the last years, Learning to Rank (LtR) had a significant influence on several tasks in the Information Retrieval field, with large research efforts coming both from the academia and the industry. Indeed, efficiency requirements must be fulfilled in order to make an effective research product deployable within an industrial environment. The evaluation of a model can be too expensive due to its size, the features used and several other factors. This tutorial discusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time
LSDS-IR'11: The 9th Workshop on Large-scale and Distributed Systems for Information Retrieval
No full textThe growth of the Web and user bases lead to important performance problems for large-scale Web search engines. The LSDS- IR '11 workshop focuses on research contributions related to the scalability and efficiency of distributed information retrieval (IR) systems. The workshop also encourages contributions that propose different ways of leveraging diversity and multiplicity of resources available in distributed systems. More specifically, we are interested in novel applications, models, and architectures that deal with efficiency and scalability of distributed IR systems. © 2011 Authors
Dexter: an open source framework for entity linking
No full textWe introduce Dexter, an open source framework for entity linking. The entity linking task aims at identifying all the small text fragments in a document referring to an entity contained in a given knowledge base, e.g., Wikipedia. The annotation is usually organized in three tasks. Given an input document the first task consists in discovering the fragments that could refer to an entity. Since a mention could refer to multiple entities, it is necessary to perform a disambiguation step, where the correct entity is selected among the candidates. Finally, discovered entities are ranked by some measure of relevance. Many entity linking algorithms have been proposed, but unfortunately only a few authors have released the source code or some APIs. As a result, evaluating today the performance of a method on a single subtask, or comparing different techniques is difficult. In this work we present a new open framework, called Dexter, which implements some popular algorithms and provides all the tools needed to develop any entity linking technique. We believe that a shared framework is fundamental to perform fair comparisons and improve the state of the art
Mining Top-K Patterns from Binary Datasets in presence of Noise
No full textThe discovery of patterns in binary dataset has many applications, e.g. in electronic commerce, TCP/IP networking, Web usage logging, etc. Still, this is a very challenging task in many respects: overlapping vs. non overlapping patterns, presence of noise, extraction of the most important patterns only. In this paper we formalize the problem of discovering the Top-K patterns from binary datasets in presence of noise, as the minimization of a novel cost function. According to the Minimum Description Length principle, the proposed cost function favors succinct pattern sets that may approximately describe the input data. We propose a greedy algorithm for the discovery of Patterns in Noisy Datasets, named PaNDa, and show that it outperforms related techniques on both synthetic and real-world data. Copyright © by SIAM
LSDS-IR ’10: 8th workshop on large-scale distributed systems for information retrieval.
No full textThe size of theWeb as well as user bases of search systems continue to grow exponentially. Consequently, providing subsecond query response times and high query throughput become quite challenging for large-scale information retrieval systems. Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval systems. The 8th Workshop on Large-Scale Distributed Systems for Information Retrieval (LSDS-IR'10) has provided a venue to discuss the current research challenges and identify new directions for distributed information retrieval. The workshop contained two industry talks as well as six research paper presentations. The hot topics in this year's workshop were collection selection architectures, application of MapReduce to information retrieval problems, similarity search, geographically distributed web search, and optimization techniques for search efficiency.</jats:p
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