End-to-End Negation Resolution as Graph Parsing

Abstract

We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem. Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representationspecific heuristics. In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution. Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model.

Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary

“…Future work may address multilingual approaches such as the training setup used by UDify or the recently proposed UDapter (Üstün et al, 2020), which aims at boosting performance of low-resource languages while keeping performance of high-resource languages high. Furthermore, it would be interesting to see if our results about biaffine achitectures also hold for non-syntactic tasks that have recently been framed as dependency parsing tasks, such as Named Entity Recognition (Yu et al, 2020), negation scope detection (Kurtz et al, 2020) or Semantic Role Labeling (Shi et al, 2020).…”

Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

“…Future work may address multilingual approaches such as the training setup used by UDify or the recently proposed UDapter (Üstün et al, 2020), which aims at boosting performance of low-resource languages while keeping performance of high-resource languages high. Furthermore, it would be interesting to see if our results about biaffine achitectures also hold for non-syntactic tasks that have recently been framed as dependency parsing tasks, such as Named Entity Recognition (Yu et al, 2020), negation scope detection (Kurtz et al, 2020) or Semantic Role Labeling (Shi et al, 2020).…”

Structured Sentiment Analysis as Dependency Graph Parsing

“…In the future, we would like to better exploit the similarities between dependency parsing and sentiment graph parsing, either by augmenting the token-level representations with contextualized vectors from their heads in a dependency tree (Kurtz et al, 2020) or by multi-task learning to dependency parse. We would also like to explore different graph parsing approaches, e.g., PERIN (Samuel and Straka, 2020).…”

“…Moreover, we cast sentiment analysis as a dependency graph parsing problem, where the sentiment expression is the root node, and the other elements have arcs which model the relationships between them. This methodology also enables us to take advantage of recent improvements in semantic dependency parsing (Dozat and Manning, 2018;Kurtz et al, 2020) to efficiently learn a sentiment graph parser.…”

“…While much of the early research on parsing these new structures (Oepen et al, 2014(Oepen et al, , 2015 focused on specialized decoding algorithms, Dozat and Manning (2018) presented a neural dependency parser that essentially relies only on its neural network structure to predict any type of dependency graph without restrictions to certain structures. Using the parser's ability to learn arbitrary dependency graphs, Kurtz et al (2020) phrased the task of negation resolution (Morante and Blanco, 2012;Morante and Daelemans, 2012) as a graph parsing task. This transformed the otherwise flat representations to dependency structures that directly encode the often overlapping relations between the building blocks of multiple negation instances at the same time.…”