A Modality Lexicon and its use in Automatic Tagging

TL;DR

This paper presents a new modality lexicon and two automated taggers that improve semantic annotation and machine translation quality by identifying modality components in language data.

Contribution

We developed a semi-automatically expanded modality lexicon and two taggers, demonstrating improved annotation precision and translation quality in machine translation tasks.

Findings

Structure-based tagger achieves 86% precision

Modality annotation improves translation quality by 0.3 Bleu points

Lexicon is publicly available for research use

Abstract

This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger---a structure-based tagger---results in precision around 86% (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.