Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

What drives content tagging: the case of photos on Flickr

Abstract: We examine tagging behavior on Flickr, a public photo-sharing website. We build on previous qualitative research that exposed a taxonomy of tagging motivations, as well as on social presence research. The motivation taxonomy suggests that motivations for tagging are tied to the intended target audience of the tags --- the users themselves, family and friends, or the general public. Using multiple data sources, including a survey and independent system data, we examine which motivations are associated with tagging level, and estimate the magnitude of their contribution. We find that the levels of the Self and Public motivations, together with social presence indicators, are positively correlated with tagging level; Family & Friends motivations are not significantly correlated with tagging. The findings and the use of survey method carry implications for designers of tagging and other social systems on the web.

Discriminative Reordering with Chinese Grammatical Relations Features

Abstract: The prevalence in Chinese of grammatical structures that translate into English in different word orders is an important cause of translation difficulty. While previous work has used phrase-structure parses to deal with such ordering problems, we introduce a richer set of Chinese grammatical relations that describes more semantically abstract relations between words. Using these Chinese grammatical relations, we improve a phrase orientation classifier (introduced by Zens and Ney (2006)) that decides the ordering of two phrases when translated into English by adding path features designed over the Chinese typed dependencies. We then apply the log probability of the phrase orientation classifier as an extra feature in a phrase-based MT system, and get significant BLEU point gains on three test sets: MT02 (+0.59), MT03 (+1.00) and MT05 (+0.77). Our Chinese grammatical relations are also likely to be useful for other NLP tasks.

Ischemia modified albumin changes - review and clinical implications.

Abstract: Ischemia modified albumin (IMA), as measured using the albumin cobalt binding test, is currently the most promising biomarker for early detection of ischemia before the onset of irreversible cardiac injury. This paper reviews the information available on IMA, including its pathophysiology, analysis, clinical applications and future perspectives. The data provided was identified by a search of MEDLINE using the terms IMA, biomarkers and ischemia. IMA may be useful to cover the complete diagnostic window of patients presenting with acute coronary syndromes (ACS) in the Emergency Department, along with the electrocardiogram and cardiac troponins. Preliminary data regarding the significance of IMA in the prognosis of either ACS or following revascularization need further study.

Collaborative competitive filtering: learning recommender using context of user choice

Abstract: While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three real-world recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.

Mentha spicata Essential Oil: Chemical Composition, Antioxidant and Antibacterial Activities against Planktonic and Biofilm Cultures of Vibrio spp. Strains

Abstract: Chemical composition, antioxidant and anti-Vibrio spp. activities of the essential oil isolated from the aerial parts of Mentha spicata L. (spearmint) are investigated in the present study. The effect of the essential oil on Vibrio spp. biofilm inhibition and eradication was tested using the XTT assay. A total of 63 chemical constituents were identified in spearmint oil using GC/MS, constituting 99.9% of the total identified compounds. The main components were carvone (40.8% ± 1.23%) and limonene (20.8% ± 1.12%). The antimicrobial activity against 30 Vibrio spp. strains (16 species) was evaluated by disc diffusion and microdilution assays. All microorganisms were strongly affected, indicating an appreciable antimicrobial potential of the oil. Moreover, the investigated oil exhibited high antioxidant potency, as assessed by four different tests in comparison with BHT. The ability of the oil, belonging to the carvone chemotype, to inhibit or reduce Vibrio spp. biofilm warrants further investigation to explore the use of natural products in antibiofilm adhesion and reinforce the possibility of its use in the pharmaceutical or food industry as a natural antibiotic and seafood preservative against Vibrio contamination.