This paper describes how the field of Combinatorial Semantics has contributed
to the design of three prototypes for the automatic generation of argument
patterns in nominal phrases in Spanish, French and German (Xera, Combinatoria
and CombiContext). It also shows the importance of knowing about the argument
syntactic-semantic interface in a production situation in the context of
foreign languages. After a descriptive section on the design, typologie and
information levels of the resources, there follows an explanation of the
central role of the combinatorial meaning (roles and ontological features). The
study deals with different semantic f ilters applied in the selection,
organization and expansion of the lexicon, being these key pieces for the
generation of grammatically correct and semantically acceptable mono- and
biargumental nominal phrases.

本论文描述了组合语义学如何在西班牙语、法语和德语（Xera、组合和 CombiContext）的自动论证模式生成的三个原型设计方面做出了贡献，并展示了在外语环境下在生产情境中了解论证句法语义界面的重要性；文章讨论了词汇资源的设计、分类和信息层次，以及组合意义（角色和本体特征）的核心作用，同时探讨了应用于词汇选择、组织和扩展的不同语义过滤器作为生成语法正确且语义可接受的单一和双重论证名词短语的关键要素。

语义组合对多语言数字工具的贡献

Contribución de la semántica combinatoria al desarrollo de  herramientas digitales multilingües

Convolutional Sparse Coding (CSC) is a well-established image representation
model especially suited for image restoration tasks. In this work, we extend
the applicability of this model by proposing a supervised approach to
convolutional sparse coding, which aims at learning discriminative dictionaries
instead of purely reconstructive ones. We incorporate a supervised
regularization term into the traditional unsupervised CSC objective to
encourage the final dictionary elements to be discriminative. Experimental
results show that using supervised convolutional learning results in two key
advantages. First, we learn more semantically relevant filters in the
dictionary and second, we achieve improved image reconstruction on unseen data.

通过加入监督正则化项来扩展卷积稀疏编码模型并学习具有判别性的字典，从而实现更好的图像重建和更语义相关的字典元素。