Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods. 相似文献
In this study, serum metabolic profiles of mini-pigs with atherosclerosis (AS) were analyzed by LC–TOFMS. Partial least-squares to latent structure-discriminant analysis and orthogonal projection to latent structure-discriminant analysis were used for group differentiation and selection of potential biomarkers. The mini-pig disease models were constructed by feeding a high-fat diet and inducing coronary injury, in accordance with the mechanism of AS pathogenesis. To characterize the development of AS, serum samples were collected and analyzed at two time points (two and ten weeks). Separate distinct clustering of results from normal and model mini-pigs could be observed for both the two and ten-week samples. With the development of AS, the metabolism of the model mini-pigs was more substantially disturbed. Major metabolites contributing to the discrimination were fatty acids, lysophosphatidylcholines, and bile acids. These potential biomarkers are related with inflammation, oxidative stress, and abnormal lipid and energy metabolism.
Methanol steam reforming (MSR) is an attractive approach to produce hydrogen for fuel cells.Due to the limited catalyst loading volume and frequent start-ups an... 相似文献
