Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks

Abstract

Humans resolve various kinds of linguistic ambiguities by exploiting available external evidence that has been acquired from modalities besides the linguistic one. This behavior can be observed for several languages, for English or German for example. In contrast, most natural language processing systems, parsers for example, rely on linguistic information only without taking further knowledge into account. While those systems are expected to correctly handle syntactically unambiguous cases, they cannot resolve syntactic ambiguities reliably. This paper hypothesizes that parsers would be able to find non-canonical interpretations of ambiguous sentences, if they exploited external, contextual information. The proposed multi-modal system, which combines data-driven and grammar-based approaches, confirmed this hypothesis in experiments on syntactically ambiguous sentences. This work focuses on the scarcely investigated relative clause attachment ambiguity instead of prepositional phrase attachment ambiguities, which are already well known in the literature. Experiments were conducted for English, German and Turkish and dynamic, i. e. highly dissimilar, contexts.

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