End-to-End Negation Resolution as Graph Parsing (2020)

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 representation-specific 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.

## introduction negation resolution (nr), the task of detecting negation and determining its scope, is relevant for a large number of applications in natural language processing, and has been the subject of several contrastive research efforts @xcite @xcite . in this paper we cast nr as a graph parsing problem. more specifically, we represent negation cues and corresponding scopes as a bi-lexical graph and learn to predict this graph from the tokens. under this representation, we may apply any dependency graph parser to the task of negation resolution. the specific parsing architecture that we use in this paper extends that of @xcite . paper structure in the following section 2, we review selected related work on negation resolution. section 3 describes the specific nr task that we address in this paper. in section 4 we present our new encoding of negations and our parsing model, followed by the description of our experiments and results in section 5. we discuss these results in section 6 and summarize our findings in section 7. ## related work while there exist a variety of datasets that annotate negation @xcite , the bio-scope @xcite and conan doyle datasets (conandoyle-neg; morante and daelemans, 2012) are most commonly used for evaluation. the latter was created for the shared task at *sem 2012 @xcite , where competing systems needed to predict both negation cues (linguistic expressions of negation) and their corresponding scopes, i.e. the part of the utterance being negated. cues can be simple negation markers (such as not or without), but may also consist of multiple words (i.e. neither . . . nor), or be mere affixes (i.e. infrequent or clueless). in contrast to other datasets, conandoyle-neg also annotates negated events that are part of the scopes. the analysis of negation is divided into two related sub-tasks, cue detection and scope resolution. while cue detection is mostly dependent on lexical or morphological features, relating cues to scopes is a structured prediction problem and will likely benefit from an analysis of morpho-syntactic or surface-semantic properties. the uio 2 system sherlock @xcite , the winner of the open track of the *sem 2012 shared task, uses morpho-syntactic parts of speech and syntactic dependencies to classify tokens as either we have never gone out without keeping a sharp watch , and no one could have escaped our notice . " deviating from the original *sem 2012 setup, @xcite simplified the task to only evaluate the performance on finding scope tokens, assuming gold-standard information about negation cues. @xcite continued this trend, additionally treating each negation instance separately, and successfully used bil-stm (bidirectional long short-term memory recurrent neural networks; @xcite . recently, @xcite used pre-trained transformers @xcite , namely bert @xcite , to further improve performance, albeit on a derivative of the original dataset @xcite . using bert in a two-stage sequence-labelling approach on the original conandoyle-neg corpus and other relevant negation corpora, @xcite successfully improved previous results by a considerable margin. the 2018 follow-up to the epe shared task @xcite again used sher-lock to evaluate parsing performance, this time restricting itself to participating systems in the colocated 2018 conll shared task on universal dependency parsing @xcite . ## task and data we target the original *sem 2012 shared task and aim to predict both negation cues and their scopes. we compare our approach with the baseline sher-lock system and the state-of-the-art systems identified through the epe shared tasks. ## approach in this section we define our graph-based encoding of negation structures, and present our parsing system and training procedure. ## experiments in this section we describe our experiments and review our baselines, methodology, and reported results. ## discussion in this section we discuss the results of our experiments and place them in the broader context of the research literature on negation resolution. ## conclusion we have introduced a novel approach to negation resolution that remodels negation annotations into dependency-style graph structures. these negation graphs directly encode the pairwise cue-scope relationships, and thus enable our neural network to more easily learn them. we extended an already powerful neural graph-parsing approach further to additionally use arbitrary dependency graph structures as inputs. to validate our method, we revisited the epe 2017 and 2018 shared tasks and the full *sem 2012 shared task on negation resolution, clearly outperforming each previously best system, albeit none of our results is statistically significant. we believe that our approach can be used to restructure other tasks as dependency-style graphs in similar fashion, and thus reuse existing systems as general purpose tools. recasting the negation resolution task as a graph-parsing problem allows us to straightforwardly use a variety of such tools. with most of these now using neural networks, we can extend them to employ massive pre-trained models such as bert @xcite or elmo @xcite . this would allow us to leverage their general power into more specific tasks that have only limited data available.