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Felipe Goldstein
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Recognizing Multimodal Entailment (tutorial at ACL 2021)
Afsaneh Hajiamin Shirazi
Blaž Bratanič
Christina Liu
Gabriel Fedrigo Barcik
Georg Fritz Osang
Jared Frank
Lucas Smaira
Ricardo Abasolo Marino
Roma Patel
Vaiva Imbrasaite
(2021) (to appear)
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In the recent years, a new form of content type has become ubiquitous in the web. These are small and noisy text snippets, created by users of social networks such as Twitter and Facebook. The full interpretation of those microposts by machines impose tremendous challenges, since they strongly rely on context. In this paper we propose a task which is much simpler than full interpretation of microposts: we aim to build classification systems to detect keywords that unambiguously refer to a single dominant concept, even when taken out of context. For example, in the context of this task, apple would be classified as ambiguous whereas microsoft would not. The contribution of this work is twofold. First, we formalize this novel classification task that can be directly applied for extracting information from microposts. Second, we show how high precision classifiers for this problem can be built out of Web data and search engine logs, combining traditional information retrieval metrics, such as inverted document frequency, and new ones derived from search query logs. Finally, we have proposed and evaluated relevant applications for these classifiers, which were able to meet precision ≥ 72% and recall ≥ 56% on unambiguous keyword extraction from microposts. We also compare those results with closely related systems, none of which could outperform those numbers.
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A Software Transactional Memory System for an Asymmetric Processor Architecture
Alexandro Baldassin
Paulo Centoducatte
Rodolfo Azevedo
Leonardo A.G. Garcia
SBAC-PAD, IEEE Computer Society(2008), pp. 175-182
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Due to the advent of multi-core processors and the consequent need for better concurrent programming abstractions, new synchronization paradigms have emerged. A promising one, known as software transactional memory (STM), aims to use transactions as the key synchronization mechanism to ease program development as well as increase its performance. Many (if not all) of the current STM implementations target homogeneous architectures. In this paper we describe an implementation of an STM system for an asymmetric architecture, the Cell BE. We evaluated our Transactional Software Cache (TSC) mechanism using a well-known micro-benchmark (IntSet) and the Genome application from STAMP. The results show that an STM implementation for the Cell architecture is feasible if the shared-memory programming model is adopted. When compared to a conventional lock-based implementation, the STM version of Genome obtained a performance gain of 84% and 24% with large and small input sets, respectively.
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