Selectional Preference

The absence of training data is a real prob- lem for corpus-based approaches to sense disambiguation, one that is unlikely to be solved soon. Selectional preference is tra- ditionally connected …...

We introduce tiered clustering, a mixture model capable of accounting for varying de- grees of shared (context-independent) fea- ture structure, and demonstrate its applicabil- ity to inferring distributed representations of …...

Most NLP systems make predictions based solely on linguistic (textual or spo- ken) input. We show how to use visual information to make better linguistic pre- dictions. We focus on …...

This study calculates the selectional prefer- ence strength between transitive verbs and their co-occurring objects, and thereby in- vestigates how much they are co-related to each other in Korean. The …...

We present a comparison of different selec- tional preference models and evaluate them on an automatic verb classification task in German. We find that all the models we compare are …...

Implicit Semantic Role Labeling is a challenging task: it requires high-level understanding of the text while annotated data is very limited. Due to the lack of training data, most researches …...

AbstractThis paper presents an investigation on the distribution of word vectors belonging to a certain word class in a pre-trained word vector space. To this end, we made several assumptions …...

AbstractWe present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector …...