Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible, source codes and corpora are available on GitHub.Comment: Accepted & forthcoming at ITNG-201

Bis(μ-naphthalene-1,8-dicarboxyl­ato)bis­[aqua­(2,2′-bipyridine)zinc(II)] tetra­hydrate

The title complex, [Zn2(C12H6O4)2(C10H8N2)2(H2O)2]·4H2O, is a binuclear complex with two independent ZnII ions in a slightly disorted trigonal bipyramidal environment, coordinated by one aqua ligand, two naphthalene-1,8-dicarboxyl­ate ligands and one 2,2′-bipyridine ligand. π–π Inter­actions [centroid–centroid distance of 3.8489 (5) Å] and O—H⋯O hydrogen bonds connect the mol­ecules, forming a three-dimensional structure

Green credit policy and corporate climate risk exposure

This paper investigates the effects of green credit policies on corporate climate risk exposure and the underlying mechanisms in China. Our results show that after the introduction of green credit policies, enterprises in polluting industries experienced a notable decline in climate risk compared to their counterparts. Further analysis reveals that the effectiveness of green credit policies in mitigating corporate climate risks can be attributed to their capacity to foster green technological innovation, refine investment strategies, facilitate the process of digitalization, and enhance the visibility of environmental issues among analysts. Moreover, we find that climate risk shaping policies vary significantly among firms, with particularly pronounced impacts on financially constrained and state-owned enterprises. This study provides critical insights for policymakers aiming to address climate challenges and bolster green financial strategies

Improving Entity Linking through Semantic Reinforced Entity Embeddings

Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality. FGS2EE first uses the embeddings of semantic type words to generate semantic embeddings, and then combines them with existing entity embeddings through linear aggregation. Extensive experiments show the effectiveness of such embeddings. Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.Comment: 6 pages, 3 figures, ACL 202

Log-Poisson Hierarchical Clustering of Cosmic Neutral Hydrogen and Ly-alpha Transmitted Flux of QSO Absorption Spectrum

we study, in this paper, the non-Gaussian features of the mass density field of neutral hydrogen fluid and the Ly-alpha transmitted flux of QSO absorption spectrum from the point-of-view of self-similar log-Poisson hierarchy. It has been shown recently that, in the scale range from the onset of nonlinear evolution to dissipation, the velocity and mass density fields of cosmic baryon fluid are extremely well described by the She-Leveque's scaling formula, which is due to the log-Poisson hierarchical cascade. Since the mass density ratio between ionized hydrogen to total hydrogen is not uniform in space, the mass density field of neutral hydrogen component is not given by a similar mapping of total baryon fluid. Nevertheless, we show, with hydrodynamic simulation samples of the concordance $\Lambda$CDM universe, that the mass density field of neutral hydrogen, is also well described by the log-Poisson hierarchy. We then investigate the field of Ly$\alpha$ transmitted flux of QSO absorption spectrum. Due to redshift distortion, Ly$\alpha$ transmitted flux fluctuations are no longer to show all features of the log-Poisson hierarchy. However, some non-Gaussian features predicted by the log-Poisson hierarchy are not affected by the redshift distortion. We test these predictions with the high resolution and high S/N data of quasars Ly$\alpha$ absorption spectra. All results given by real data, including $\beta$-hierarchy, high order moments and scale-scale correlation, are found to be well consistent with the log-Poisson hierarchy. We compare the log-Poisson hierarchy with the popular log-normal model of the Ly$\alpha$ transmitted flux. The later is found to yield too strong non-Gaussianity at high orders, while the log-Poisson hierarchy is in agreement with observed data.Comment: 24 pages, 9 figures, accepted by Ap