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

Capturing Ambiguity in Crowdsourcing Frame Disambiguation

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence. Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.Comment: in publication at the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 201

Finding common ground: towards a surface realisation shared task

In many areas of NLP reuse of utility tools such as parsers and POS taggers is now common, but this is still rare in NLG. The subfield of surface realisation has perhaps come closest, but at present we still lack a basis on which different surface realisers could be compared, chiefly because of the wide variety of different input representations used by different realisers. This paper outlines an idea for a shared task in surface realisation, where inputs are provided in a common-ground representation formalism which participants map to the types of input required by their system. These inputs are derived from existing annotated corpora developed for language analysis (parsing etc.). Outputs (realisations) are evaluated by automatic comparison against the human-authored text in the corpora as well as by human assessors

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