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This paper presents an end-to-end question answering system for legal texts. This system includes two main phases. In the first phase, our system will retrieve articles from Japanese Civil Code that are relevant with the given question using the cosine distance after the given question and articles are converted into vectors using TF-IDF weighting scheme. Then, a ranking model can be applied to re-rank these retrieved articles using a learning to rank algorithm and annotated corpus. In the second phase, we adapted two deep learning models, which has been proposed for the Natural language inference task, to check the entailment relationship between a question and its related articles including a sentence encoding-based model and a decomposable attention model. Experimental results show that our approaches can be a promising approach for information extraction/entailment in legal texts.

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In Lightweight Aggregate Concrete manufacturing, there is a stratification of lightweight aggregate during construction process, because bulk density of lightweight aggregates is much smaller than that of cement mortar. This paper presents some research results about the effect of air entrainment additive on the properties of lightweight aggregate concrete. When adding air entrainment admixture into lightweight concrete components with the amount of 0.02 – 0.08% by mass of binder, the stratification and bulk density of fresh concrete reduce, and strength of hardened concrete is also significantly reduced. Therefore, this result indicates that we should only use the air entrainment additive in small amounts (less than 0.02% by mass).