Treebank is an important resource for both research and application of natural language processing. For Vietnamese, we still lack such kind of corpora. This paper presents up-to-date results of a project for Vietnamese treebank construction. Since Vietnamese is an isolating language and has no word delimiter, there are many ambiguities in sentence analysis. We systematically applied a lot of linguistic techniques to handle such ambiguities. Annotators are supported by automaticlabeling tools and a tree-editor tool. Raw texts are extracted from Tuoi Tre (Youth), an online Vietnamese daily newspaper. The current annotation agreement is around 90 percent.
Silica nanoparticles (SiO2 NPs) synthesized by the Stober method were used as drug delivery vehicles. Doxorubicin hydrochloride (DOX·HCl) is a chemo-drug absorbed onto the SiO2 NPs surfaces. The DOX·HCl loading onto and release from the SiO2 NPs was monitored via UV-VIS and fluorescence spectra. Alternatively, the zeta potential was also used to monitor and evaluate the DOX·HCl loading process. The results showed that nearly 98% of DOX·HCl was effectively loaded onto the SiO2 NPs’ surfaces by electrostatic interaction. The pH-dependence of the process wherein DOX·HCl release out of DOX·HCl-SiO2 NPs was investigated as well. For comparison, both the free DOX·HCl molecules and DOX·HCl-SiO2 NPs were used as the labels for cultured cancer cells. Confocal laser scanning microscopy images showed that the DOX·HCl-SiO2 NPs were better delivered to cancer cells which are more acidic than healthy cells. We propose that engineered DOX·HCl-SiO2 systems are good candidates for drug delivery and clinical applications.
In this paper, we study semantic role labelling (SRL), a subtask of semantic parsing of natural language sentences and its application for the Vietnamese language. We present our effort in building Vietnamese PropBank, a first Vietnamese SRL corpus and a software system for labelling semantic roles of Vietnamese texts. In particular, we present a novel constituent extraction algorithm in the argument candidate identification step which is more suitable and more accurate than a common node-mapping method. In the learning machine part, our system integrates distributed word features produced by two recent unsupervised learning models in two learned statistical classifiers and makes use of integer linear programming inference procedure to improve the accuracy. The system is evaluated in a series of experiments and achieves a good result, an $F_1$ score of 74.77\%. Our system, including corpus and software, is available as an open source project for free research and we believe that it is a good baseline for the development of future Vietnamese SRL systems.
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