Guidelines for annotating the LUNA corpus with frame information

This document defines the annotation workflow aimed at adding frame information to the LUNA corpus of conversational speech. In particular, it details both the corpus pre-processing steps and the proper annotation process, giving hints about how to choose the frame and the frame element labels. Besides, the description of 20 new domain-specific and language-specific frames is reported. To our knowledge, this is the first attempt to adapt the frame paradigm to dialogs and at the same time to define new frames and frame elements for the specific domain of software/hardware assistance. The technical report is structured as follows: in Section 2 an overview of the FrameNet project is given, while Section 3 introduces the LUNA project and the annotation framework involving the Italian dialogs. Section 4 details the annotation workflow, including the format preparation of the dialog files and the annotation strategy. In Section 5 we discuss the main issues of the annotation of frame information in dialogs and we describe how the standard annotation procedure was changed in order to face such issues. Then, the 20 newly introduced frames are reported in Section 6

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

Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.Comment: EMNLP 2017 (long paper

An Ontology-Based Assistant For Analyzing Agents\u27 Activities

This thesis reports on work in progress on software that helps an analyst identify and analyze activities of actors (such as vehicles) in an intelligence-relevant scenario. A system is being developed to aid intelligence analysts, IAGOA ((Intelligence Analyst’s Geospatial and Ontological Assistant). Analysis may be accomplished by retrieving simulated satellite data of ground vehicles and interacting with software modules that allow the analyst to conjecture the activities in which the actor is engaged along with the (largely geospatial and temporal) features of the area of operation relevant to the natures of those activities. Activities are conceptualized by ontologies. The research relies on natural language components (semantic frames) gathered from the FrameNet lexical database, which captures the semantics of lexical items with an ontology using OWL. The software has two components, one for the analyst, and one for a modeler who produces HTML and parameterized KML documents used by the analyst. The most significant input to the modeler software is the FrameNet OWL file, and the interface for the analyst and, to some extent, the modeler is provided by the Google Earth API