We propose a method for extracting semantic orientations of words: desirable or undesirable. Regarding semantic orientations as spins of electrons, we use the mean field approximation to compute the approximate probability function of the system instead of the intractable actual probability function. We also propose a criterion for parameter selection on the basis of magnetization. Given only a small number of seed words, the proposed method extracts semantic orientations with high accuracy in the experiments on English lexicon. The result is comparable to the best value ever reported.
Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based methods and two learning-based methods.1 Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.
ABSTRACT:In this study, we investigated whether components of pomegranate could inhibit CYP3A-mediated drug metabolism. The ability of pomegranate to inhibit the carbamazepine 10,11-epoxidase activity of CYP3A was examined using human liver microsomes, and pomegranate juice was shown to be a potent inhibitor of human CYP3A. Addition of 25 l (5.0% v/v) of pomegranate juice resulted in almost complete inhibition of the carbamazepine 10,11-epoxidase activity of human CYP3A (1.8%). The inhibition potency of pomegranate juice was similar to that of grapefruit juice. In addition, we investigated the in vivo interaction between pomegranate juice and carbamazepine pharmacokinetics using rats. In comparison with water, the area under the concentration-time curve (AUC) of carbamazepine was approximately 1.5-fold higher when pomegranate juice (2 ml) was orally injected 1 h before the oral administration of the carbamazepine (50 mg/kg). On the other hand, the elimination half-life of carbamazepine and the AUC ratio of carbamazepine 10,11-epoxide to carbamazepine were not altered by the injection of pomegranate juice. These data suggest that pomegranate juice component(s) impairs the function of enteric but not hepatic CYP3A. Thus, we discovered that a component(s) of pomegranate inhibits the human CYP3A-mediated metabolism of carbamazepine. Furthermore, pomegranate juice alters the carbamazepine pharmacokinetics in rats.
Some downstream NLP tasks exploit discourse dependency trees converted from RST trees. To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures. Thus, we propose a novel neural top-down RST parsing method. Then, we exploit three levels of granularity in a document, paragraphs, sentences and Elementary Discourse Units (EDUs), to parse a document accurately and efficiently. The parsing is done in a top-down manner for each granularity level, by recursively splitting a larger text span into two smaller ones while predicting nuclearity and relation labels for the divided spans. The results on the RST-DT corpus show that our method achieved the state-of-the-art results, 87.0 unlabeled span score, 74.6 nuclearity labeled span score, and the comparable result with the state-of-the-art, 60.0 relation labeled span score. Furthermore, discourse dependency trees converted from our RST trees also achieved the state-of-the-art results, 64.9 unlabeled attachment score and 48.5 labeled attachment score.
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