Part of speech (POS) tagging is a familiar NLP task. State of the art taggers
routinely achieve token-level accuracies of over 97% on news body text,
evidence that the problem is well understood. However, the register of English
news headlines, "headlinese", is very different from the register of long-form
text, causing POS tagging models to underperform on headlines. In this work, we
automatically annotate news headlines with POS tags by projecting predicted
tags from corresponding sentences in news bodies. We train a multi-domain POS
tagger on both long-form and headline text and show that joint training on both
registers improves over training on just one or naively concatenating training
sets. We evaluate on a newly-annotated corpus of over 5,248 English news
headlines from the Google sentence compression corpus, and show that our model
yields a 23% relative error reduction per token and 19% per headline. In
addition, we demonstrate that better headline POS tags can improve the
performance of a syntax-based open information extraction system. We make POSH,
the POS-tagged Headline corpus, available to encourage research in improved NLP
models for news headlines.

本研究通过对英语新闻标题进行词性标注，研究了长文本和标题文本之间的词性标注模型，在使用自动标注的新语料库进行验证后，相较于训练单个注册器或者拼接培训集的方法，展示了注册器相互联合培训的显著提升，并且成果表明改进高质量新闻标题的词性标注有助于语法基础的开放信息提取系统的性能。