Semantic role labeling (SRL) and word sense disambiguation (WSD) are two fundamental tasks in natural language processing to find a sentence-level semantic representation. To date, they have mostly been modeled in isolation. However, this approach neglects logical constraints between them. We therefore exploit some pipeline systems which verify the automatic all word sense disambiguation could help the semantic role labeling and vice versa. We further propose a Markov logic model that jointly labels semantic roles and disambiguates all word senses. By evaluating our model on the OntoNotes 3.0 data, we show that this joint approach leads to a higher performance for word sense disambiguation and semantic role labeling than those pipeline approaches. 1

In many applications, replacing a complex word form by its stem can reduce sparsity, revealing connections in the data that would not otherwise be apparent. In this paper, we focus on prefix verbs: verbs formed by adding a prefix to an existing verb stem. A prefix verb is considered compositional if it can be decomposed into a semantically equivalent expression involving its stem. We develop a classifier to predict compositionality via a range of lexical and distributional features, including novel features derived from web-scale N-gram data. Results on a new annotated corpus show that prefix verb compositionality can be predicted with high accuracy. Our system also performs well when trained and tested on conventional morphological segmentations of prefix verbs. 1