| Conclusions | This work adds unsupervised semantic role labeling to the list of NLP tasks benefiting from the crosslingual induction setting. |
| Introduction | Semantic role labeling (SRL) (Gildea and Juraf-sky, 2002) involves predicting predicate argument structure, i.e. |
| Introduction | For example, in our sentences (a) and (b) representing so-called blame alternation (Levin, 1993), the same information is conveyed in two different ways and a successful model of semantic role labeling needs to learn the corresponding linkings from the data. |
| Problem Definition | As we mentioned in the introduction, in this work we focus on the labeling stage of semantic role labeling . |
| Problem Definition | In sum, we treat the unsupervised semantic role labeling task as clustering of argument keys. |