La Liga president Javier Tebas does not have a good relationship with Real Madrid but still acknowledged in 2021 that “it is the (club) that is in the best financial health” and the one that “has best managed the pandemic”. Shopping for Ten Hag and Manchester United in 2023: Strikers.With Kylian Mbappe now being available for sale due to the deterioration of his relationship with Paris Saint-Germain, there could be another substantial transfer fee on the cards for Madrid, as well as having to meet the France striker’s substantial demands for signing-on fee and salary.Īll of which raises the question of how might Madrid manage to afford this while they are also completing a €1billion rebuild of their stadium, and with club president Florentino Perez regularly claiming they can no longer afford to compete at the top of the transfer market.
Not counting Barcelona’s €60million signing of Vitor Roque, which will not go through until next January, Madrid have so far this summer spent more than all the division’s other 19 clubs put together.Īnd yet there is a chance for even greater spending from the Bernabeu. Experiments on three real-world datasets show that the proposed TextTruth model can accurately select trustworthy answers, even when these answers are formed by multiple factors.No La Liga team has come close to this level of outlay. The proposed method works in an unsupervised manner, and thus can be applied to various application scenarios that involve text data. After that, the answers to each question can be ranked based on the estimated trustworthiness of factors. To tackle these challenges, in this paper, we propose a novel truth discovery method, named “TextTruth”, which jointly groups the keywords extracted from the answers of a specific question into multiple interpretable factors, and infers the trustworthiness of both answer factors and answer providers. The major challenges of inferring true information on text data stem from the multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning).
However, most existing truth discovery methods are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data as text data has its unique characteristics. Truth discovery has attracted increasingly more attention due to its ability to distill trustworthy information from noisy multi-sourced data without any supervision. TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data Hengtong Zhang (SUNY at Buffalo) Yaliang Li (Baidu Research) Fenglong Ma (SUNY Buffalo) Jing Gao (University at Buffalo) Lu Su (The State University of New York at Buffalo)
0 kommentar(er)
