739 research outputs found
VerbAtlas: a novel large-scale verbal semantic resource and its application to semantic role labeling
We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments.
We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org
Event-based Access to Historical Italian War Memoirs
The progressive digitization of historical archives provides new, often
domain specific, textual resources that report on facts and events which have
happened in the past; among these, memoirs are a very common type of primary
source. In this paper, we present an approach for extracting information from
Italian historical war memoirs and turning it into structured knowledge. This
is based on the semantic notions of events, participants and roles. We evaluate
quantitatively each of the key-steps of our approach and provide a graph-based
representation of the extracted knowledge, which allows to move between a Close
and a Distant Reading of the collection.Comment: 23 pages, 6 figure
Robust Semantic Parsing with Adversarial Learning for Domain Generalization
International audienceThis paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for the integration of Semantic Parsing technologies in real applications. The underlying question throughout this study is whether adversarial learning can be used to train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations.We propose to perform Semantic Parsing with a domain classification adversarial task without explicit knowledge of the domain. The strategy is first evaluated on a French corpus of encyclopedic documents, annotated with FrameNet, in an information retrieval perspective, then on PropBank Semantic Role Labeling task on the CoNLL-2005 benchmark. We show that adversarial learning increases all models generalization capabilities both on in and out-of-domain data
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