论文标题
词汇语义识别
Lexical Semantic Recognition
论文作者
论文摘要
在词汇语义中，尽管它们相互依存关系，但通常会单独处理各种现象的全句分割和段标记。我们假设统一的词汇语义识别任务是封装先前不同注释样式的有效方法，包括多词表达式识别 /分类和超级义标记。使用Streusle语料库，我们训练神经CRF序列标记器，并沿着各种注释轴进行评估其性能。随着标签设置概括了以前的任务（Parseme，dimsum），我们还评估了该模型对这些测试集的推广程度，发现尽管仅在Streusle上进行了培训，但仍发现它接近或超过了现有模型。我们的工作还建立了基线模型和评估指标，用于对词汇语义的集成和准确建模，从而促进了该领域的未来工作。
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.
