Research on the problem of morphological disambiguation of Arabic has noted that techniques developed for lexical disambiguation in English do not easily transfer over, since the affixation present in Arabic creates a very different tag set than for English, encoding both inflectional morphology and more complex tokenization sequences. This work takes a new approach to this problem based on a distinction between the open-class and closed-class categories of tokens, which differ both in their frequencies and in their possible morphological affixations. This separation simplifies the morphological analysis problem considerably, making it possible to use a Conditional Random Field model for joint tokenization and “core” part-of-speech tagging of the open-class items, while the closed-class items are handled by regular expressions. This work is therefore situated between data-driven approaches and those that use a morphological analyzer. For the tasks of tokenization and core part-of-speech tagging, the resulting system outperforms, on the given test set, a system that incorporates a morphological analyzer. We also evaluate the effects of the differences on parser performance when the tagger output is used for parser input.

A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text.

Recent work in machine translation and information extraction has demonstrated the utility of a level that represents the predicate-argument structure. It would be especially useful for machine translation to have two such Proposition Banks, one for each language under consideration. A Proposition Bank for English has been developed over the last few years, and we describe here our development of a tool for facilitating the development of a Chinese Proposition Bank. We also discuss some issues specific to the Chinese Treebank that complicate the matter of mapping syntactic representation to a predicate-argument level, and report on some preliminary evaluation of the accuracy of the semantic tagging tool.

Many theories of semantic interpretation use λ-term manipulation to compositionally compute the meaning of a sentence. These theories are usually implemented in a language such as Prolog that can simulate λ-term operations with first-order unification. However, for some interesting cases, such as a Combinatory Categorial Grammar account of coordination constructs, this can only be done by obscuring the underlying linguistic theory with the "tricks" needed for implementation. This paper shows how the use of abstract syntax permitted by higher-order logic programming allows an elegant implementation of the semantics of Combinatory Categorial Grammar, including its handling of coordination constructs.