One of the challenges of corpus querying is making sense of the results of a query, especially when a large number of results and linguistically annotated data are concerned. While the most widespread tools for querying syntactically annotated corpora tend to focus on single occurrences, one aspect that is not fully exploited yet in this area is that language is a complex system whose units are connected to each other at both microscopic (the single occurrences) and macroscopic level (the whole system itself). Assuming that language is a system, we describe a tool (using the DoubleTreeJS visualization) to visualize the results of querying dependency treebanks by forming a node from a single item type, and building a network in which the heads and the dependents of the central node are respectively the left and the right vertices of the tree, which are connected to the central node by dependency relations. One case study is presented, consisting in the exploitation of DoubleTreeJS for supporting one assumption in theoretical linguistics with evidence provided by the data of a dependency treebank of Medieval Latin.

N-gram measures of translation quality, such as BLEU and the related NIST metric, are becoming increasingly important in machine translation, yet their behaviors are not fully understood. In this paper we examine the performance of these metrics on professional human translations into German of two literary genres, the Bible and Tom Sawyer. The most surprising result is that some machine translations outscore some professional human translations. In addition, it can be difficult to distinguish some other human translations from machine translations with only two reference translations; with four reference translations it is much easier. Our results lead us to conclude that much care must be taken in using n-gram measures in formal evaluations of machine translation quality, though they are still valuable as part of the iterative development cycle.