Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for cross-linguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.

In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method.

The problem of Vietnamese syntactic parsing, especially constituency parsing, has recently been tackled by several research groups. A common effort of the Vietnamese language processing community has allowed the creation of VietTreebank, a reference parsed corpus containing about 10,000 sentences for the constituency parsing task. In this paper, we present our work to build a reference treebank, based on VietTreebank, for the dependency parsing task, which has not yet been very well studied for Vietnamese. First we define a dependency label set by adapting the dependency schema developed by the NLP group at Stanford university and taking into account the particularities of Vietnamese grammar. Then we propose an algorithm to convert a constituency treebank to a dependency one. The algorithm is tested on a set of 100 sentences of VietTreebank corpus and gives very good results. Finally, we carry out an experiment on Vietnamese dependency parsing using MaltParser tool and the dependency treebank converted from VietTreebank.

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