Breaking Bad: Extraction of Verb-Particle Constructions from a Parallel Subtitles Corpus

Abstract

The automatic extraction of verb-particle constructions (VPCs) is of particular interest to the NLP community. Previous studies have shown that word alignment methods can be used with parallel corpora to successfully extract a range of multi-word expressions (MWEs). In this paper the technique is applied to a new type of corpus, made up of a collection of subtitles of movies and television series, which is parallel in English and Spanish. Building on previous research, it is shown that a precision level of 94±4.7% can be achieved in English VPC extraction. This high level of precision is achieved despite the difficulties of aligning and tagging subtitles data. Moreover, many of the extracted VPCs are not present in online lexical resources, highlighting the benefits of using this unique corpus type, which contains a large number of slang and other informal expressions. An added benefit of using the word alignment process is that translations are also automatically extracted for each VPC. A precision rate of 75±8.5% is found for the translations of English VPCs into Spanish. This study thus shows that VPCs are a particularly good subset of the MWE spectrum to attack using word alignment methods, and that subtitles data provide a range of interesting expressions that do not exist in other corpus types.