In this paper, we argue that ordering prenominal modifiers – typically pursued as a supervised modeling task – is particularly wellsuited to semi-supervised approaches. By relying on automatic parses to extract noun phrases, we can scale up the training data by orders of magnitude. This minimizes the predominant issue of data sparsity that has informed most previous approaches. We compare sever...

In this work, we present a novel approach to the generation task of ordering prenominal modifiers. We take a maximum entropy reranking approach to the problem which admits arbitrary features on a permutation of modifiers, exploiting hundreds of thousands of features in total. We compare our error rates to the state-of-the-art and to a strong Google ngram count baseline. We attain a maximum erro...

Producing a fluent ordering for a set of prenominal modifiers in a noun phrase (NP) is a problematic task for natural language generation and machine translation systems. We present a novel approach to this issue, adapting multiple sequence alignment techniques used in computational biology to the alignment of modifiers. We describe two training techniques to create such alignments based on raw...

Our long-term goal is to contribute to the integration of formal and lexical semantics. Our more immediate theoretical starting point is the idea of “text as theory”, within a model-theoretic semantic framework. We describe a set of empirical problems in the domain of genitive modifiers which offer a challenge to theories of the integration of lexical, compositional, and contextual information....

It seems to be cross-linguistically fairly common for morphology added to DPs that are already inflected for Case to interact in different ways with structural and inherent case morphology. Such morphology is often required to replace structural case morphemes, while DPs with inherent case either cannot receive the morphology at all or simply add it on top of the existing inherent case morpheme...