Frame semantic representations have been useful in several applications ranging from text-
to-scene generation, to question answering and social network analysis. Predicting such …

Abstract Domain dependence of NLP systems is one of the major obstacles to their
application in large-scale text analysis, also restricting the applicability of FrameNet …

State-of-the-art semantic role labelling systems require large annotated corpora to achieve
full performance. Unfortunately, such corpora are expensive to produce and often do not …

The successful application of neural networks to a variety of NLP tasks has provided strong
impetus to develop end-to-end models for semantic role labeling which forego the need for …

Neural models have shown several state-of-the-art performances on Semantic Role
Labeling (SRL). However, the neural models require an immense amount of semantic-role …

Background Emergency room reports pose specific challenges to natural language
processing techniques. In this setting, violence episodes on women, elderly and children are …

This thesis details three contributions to the advancement of semantic-enriched parsing for
English sentences: inventories of semantic relations covering three semantically ambiguous …

In this paper the UNITOR-HMM-TK system participating in the Spatial Role Labeling task at
SemEval 2013 is presented. The spatial roles classification is addressed as a sequence …

In this paper, we propose innovative representations for automatic classification of verbs
according to mainstream linguistic theories, namely VerbNet and FrameNet. First, syntactic …

FrameNet is a lexical resource that provides rich semantic representations of the core
English vocabulary based on Fillmore's Frame Semantics, with more than 200k manually …