This paper aims to give a preliminary analysis of focus marking constructions in Tigrinya within the Universal Dependency (UD) framework. We identify three types of constructions that we consider clefting. All three involve a copula placed after the focus element, the UD root, and a subordinate verb form representing the presupposed information. Tigrinya has three possibilities for this subordinate verb, which we annotate as csubj, advcl, and xcomp. In each, we use the subrelation cleft to indicate the common structure and function of the different cleft types on a par with the use of subrelations pass for passive in different languages.

We are developing a treebank for Tigrinya within the Universal Dependency (UD) framework. UD proposes a set of universal grammatical relations to capture dependency relations between words in any language. However, for some classes of verbs it is not a straightforward matter to know what grammatical relations the verbs are categorized for. In this paper we discuss the decisions we have had to make for the annotation of arguments of experience verbs in the Semitic language Tigrinya, which exhibit a number of unusual morphosyntactic properties. We describe a classification of experience verb roots in the language, based on the various ways in which the core experiencer and stimulus arguments are realized syntactically and morphologically and on which valence-changing operations the roots permit. We supplement our analysis with data from a morphological analysis of a Tigrinya corpus.

For languages with complex morphology, word-to-word translation is a task with various potential applications, for example, in information retrieval, language instruction, and dictionary creation, as well as in machine translation. In this paper, we confine ourselves to the subtask of character alignment for the particular case of families of related languages with very few resources for most or all members. There are many such families; we focus on the subgroup of Semitic languages spoken in Ethiopia and Eritrea. We begin with an adaptation of the familiar alignment algorithms behind statistical machine translation, modifying them as appropriate for our task. We show how character alignment can reveal morphological, phonological, and orthographic correspondences among related languages.