2 research outputs found
Coordinate constructions in English enhanced universal dependencies: analysis and computational modeling
In this paper, we address the representation of coordinate constructions in Enhanced Universal Dependencies (UD), where relevant dependency links are propagated from conjunction heads to other conjuncts. English treebanks for enhanced UD have been created from gold basic dependencies using a heuristic rule-based converter, which propagates only core arguments. With the aim of determining which set of links should be propagated from a semantic perspective, we create a large-scale dataset of manually edited syntax graphs. We identify several systematic errors in the original data, and propose to also propagate adjuncts. We observe high inter-annotator agreement for this semantic annotation task. Using our new manually verified dataset, we perform the first principled comparison of rule-based and (partially novel) machine-learning based methods for conjunction propagation for English. We show that learning propagation rules is more effective than hand-designing heuristic rules. When using automatic parses, our neural graph-parser based edge predictor outperforms the currently predominant pipelines using a basic-layer tree parser plus converters
What Drives the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations
Given a specific discourse, which discourse properties trigger the use of
metaphorical language, rather than using literal alternatives? For example,
what drives people to say "grasp the meaning" rather than "understand the
meaning" within a specific context? Many NLP approaches to metaphorical
language rely on cognitive and (psycho-)linguistic insights and have
successfully defined models of discourse coherence, abstractness and affect. In
this work, we build five simple models relying on established cognitive and
linguistic properties -- frequency, abstractness, affect, discourse coherence
and contextualized word representations -- to predict the use of a metaphorical
vs. synonymous literal expression in context. By comparing the models' outputs
to human judgments, our study indicates that our selected properties are not
sufficient to systematically explain metaphorical vs. literal language choices.Comment: 12 pages, 6 figures, 1 table. Accepted at *SEM202