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This article describes a compositional distributional method to generate contextualized senses of words and identify their appropriate translations in the target language using monolingual corpora. Word translation is modeled in the same way as contextualization of word meaning, but in a bilingual vector space. The contextualization of meaning is carried out by means of distributional composition within a structured vector space with syntactic dependencies, and the bilingual space is created by means of transfer rules and a bilingual dictionary. A phrase in the source language, consisting of a head and a dependent, is translated into the target language by selecting both the nearest neighbor of the head given the dependent, and the nearest neighbor of the dependent given the head. This process is expanded to larger phrases by means of incremental composition. Experiments were performed on English and Spanish monolingual corpora in order to translate phrasal verbs in context. A new bilingual data set to evaluate strategies aimed at translating phrasal verbs in restricted syntactic domains has been created and released.

The separation of oxygen (O2) and nitrogen (N2) from air is a process of utmost importance nowadays, as both species are vital for numerous fundamental processes essential for our development. Membranes designed for their selective molecule separation have become the materials of choice for researchers, primarily due to their ease of use. The present study proposes grazynes, 2D carbon-based materials consisting of sp and sp2 C atoms, as suitable membranes for separating O2 and N2 from air. By combining static density functional theory (DFT) calculations with molecular dynamics (MD) simulations, we address this issue through a comprehensive examination of the thermodynamic, kinetic, and dynamic aspects of the molecular diffusions across the nano-engineered pores of grazynes. The studied grazyne structures have demonstrated the ability to physisorb both O2 and N2, preventing material saturation, with diffusion rates exceeding 1 s−1 across a temperature range of 100–500 K. Moreover, they exhibit a selectivity of ca. 2 towards O2 at 300 K. Indeed, MD simulations with equimolar mixtures of O2:N2 indicated a selectivity towards O2 in both grazynes with ca. twice as many O2 filtered molecules in the [1],[2]{2}-grazyne and with O2 representing ca. 88% of the filtered gas in the [1],[2]{(0,0),2}-grazyne. [1],[2]{2}-grazyne shows higher permeability for both molecules compared to the other grazyne, with O₂ demonstrating particularly enhanced diffusion capacity across both membranes. Further MD simulations incorporating CO2 and Ar confirm O2 enrichment, particularly with [1],[2]{(0,0),2}-grazyne, which increased the presence of O2 in the filtered mixture by 26% with no evidence of CO2 molecules. [ABSTRACT FROM AUTHOR]

The separation of carbon dioxide (CO2) from nitrogen (N2) is at the core of any global warming remediation technology aimed at reducing the CO2 content in the atmosphere. Chemical membranes designed to differentially permeate both molecules have become quite appealing due to their simple use, although many membrane‐based separations stand out as a promising solution for CO2 separation. These are environmentally friendly, with high active surface areas, compact design, easy to maintain and cost‐effective, although the field is still growing due to the difficulties in the CO2/N2 separation. The present study poses grazynes, two‐dimensional C‐based materials with sp and sp2 C atoms, aligned along stripes, as suited membranes for the CO2/N2 separation. The combination of density functional theory (DFT) and molecular dynamics (MD) simulations allow tackling the energetics, kinetics, and dynamics of the membrane effectiveness of grazynes with engineered pores for such a separation in a holistic fashion. The explored grazynes are capable of physisorbing CO2 and N2, thus avoiding material poisoning by molecular decoration, while the diffusion of CO2 through the pores is found to be rapid, yet easier than that of N2, in the rate order of the s−1 in the 100–500 K temperature range. In particular, low‐temperature CO2 separation even for CO2 contents below 0.5 % are found for [1],[2]{2}‐grazyne when controlling the membrane exposure contact to the gas mixture, paving the way for exploring and using grazynes for air CO2 remediation. [ABSTRACT FROM AUTHOR]

The availability of large web-based corpora has led to significant advances in a wide range of technologies, including massive retrieval systems or deep neural networks. However, leveraging this data is challenging, since web content is plagued by the so-called boilerplate: ads, incomplete or noisy text and rests of the navigation structure, such as menus or navigation bars. In this work, we present a novel and efficient approach to extract useful and well-formed content from web-scraped data. Our approach takes advantage of Language Models and their implicit knowledge about correctly formed text, and we demonstrate here that perplexity is a valuable artefact that can contribute in terms of effectiveness and efficiency. As amatter of fact, the removal of noisy parts leads to lighter AI or search solutions that are effective and entail important reductions in resources spent. We exemplify here the usefulness of our method with two downstream tasks, search and classification, and a cleaning task. We also provide a Python package with pre-trained models and a web demo demonstrating the capabilities of our approach. [ABSTRACT FROM AUTHOR]