This paper presents work on the creation of a Universal Dependency (UD) treebank for Wolof as the first UD treebank within the Northern Atlantic branch of the Niger-Congo languages. The paper reports on various issues related to word segmentation for tokenization and the mapping of PoS tags, morphological features and dependency relations to existing conventions for annotating Wolof. It also outlines some specific constructions as a starting point for discussing several more general UD annotation guidelines, in particular for noun class marking, deixis encoding, and focus marking.

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Clitics often involve intricate interaction between different grammar components, including morphophonology, syntax and information structure. This phenomenon is challenging from a general theoretical perspective. In Wolof, clitization has been investigated using a transformational approach. In contrast, this paper proposes a formal analysis of Wolof clitics using a non-transformational model based on the Lexical Functional Grammar (LFG) theory. This approach needs no special rule for verb movements at all and preserves the lexical integrity of words. For sake of concreteness, the theoretical purpose is combined with an implementation that illustrates how the LFG formalism is well suited to a precise treatment of clitics.

Non-compositional multi-word expressions present great challenges to natural language processing applications. In this paper, we present a method for modeling non-compositional expressions based on the assumption that the meaning of expressions depends on context. Therefore, context words can be used to select documents and separate documents where the expression has different meanings. Deviation from a baseline is measured using serendipity (i.e. the pointwise effect size). We used this statistical measure to mark which patterns are over- and under-represented and to take a decision if the pattern under scrutiny belongs to the meaning selected by the context words or not. We used the Google search engine to find document frequency estimates. When used with Google document frequency estimates, the serendipity measure closely mirrors some human intuitions on the preferred alternative.