N oun A tlas: Filling the Gap in Nominal Semantic Role Labeling (2024)

Abstract: Despite significant advances in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus needing proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although automatically created, NounAtlas – our frame inventory – is subsequently fully validated. We then put forward a technique to generate silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices which enable seamless integration of our predicate inventory with its verbal counterpart, we can mix verbal and nominal data and perform robust SRL on both types of predicates.

## introduction in the ever-evolving field of natural language processing (nlp), the pursuit of natural language understanding (nlu) has garnered increasing attention over the past two decades. among the open challenges in this domain, semantic role labeling (srl) is still far from being solved. pioneered by @xcite , srl is the task of identifying the semantic relations between predicates and their arguments (i.e. "who did what to whom, when, where, and how?"). srl has already proven to be a valuable asset, contributing to diverse tasks, such as question answering @xcite , textual inference @xcite , neural machine translation @xcite , visual semantic role labeling @xcite ), text summariza-tion @xcite and storytelling @xcite . one common oversimplification regarding srl is the assumption that most predicates are verbs. indeed, research has focused heavily on verbs as the driving force of meaning in sentences, also propelled by the development of verb-centric resources @xcite @xcite . in many contexts, however, nominal predicates are at least as important as their verbal counterparts. this phenomenon is particularly evident in media content, including newspaper headlines, blog titles, short text messages, etc. consider for instance the sentence "astounding discovery in the laboratory yesterday night: dr. jones unveils groundbreaking research findings!". in this example, the noun "discovery" serves as the predicate, indicating an action, with its arguments being "astounding" (attribute of the discovery), "in the laboratory" (location), "yesterday night" (time), and "dr. jones" (agent). such nominal predicates need to be specifically addressed in order for downstream applications to take full advantage of srl. while some resources have been provided that include nominal framesets, such as nombank @xcite and framenet @xcite , their coverage is limited and they provide either predicate-specific roles (nombank) or overspecific or incomplete frame scenarios (framenet). our work aims to fill this gap and equip semantic role labeling with both a comprehensive inventory of nominal predicates (paired with verbal ones) and annotated datasets to perform the task. in summary our contributions are the following: ## nounatlas before delving into the process of constructing nounatlas, our large-scale nominal predicate inventory, we highlight a few features in verbatlas that enable this process. verbatlas as mentioned in section 2, verbatlas uses verbal synsets from wordnet as verbal predicate proxies, organizing them into semanticallycoherent frames. this includes more than 400 synset clusters, each assigned a human-readable label, such as eat-bite and speak. a key advantage of verbatlas over its alternatives is its common prototypical argument structure and cross-frame set of semantic roles à la verbnet: for instance, the frame speak features the roles of agent and topic. importantly, being univocally associated with exactly one frame, each synset is associated with the corresponding frame semantics and predicate argument structure, which can be exploited for srl. for instance, in figure 1 the verbal synsets embarrass.v.01 and pride.v.01 are included in the cause-mental-state frame. ## creating a nominal srl dataset as a result of the work outlined in section 3.1, we obtained an inventory of nominal predicates organized into frames. however, we still have to build a large dataset for nominal semantic role labeling, i.e. annotated with our nominal predicates. in what follows, we achieve this goal by (i) collecting wordnet verbal sense occurrences (section 4.1), (ii) transforming each verbal predicate annotation into a nominal one, adjusting the sen-binary classification ranking accuracy f1-score acc@1 acc@2 acc@3 acc@4 acc@5 91.9 91.4 44.0 61.3 70.6 77.7 82.1 ## conclusions with our work, we fill the gap in nominal srl by introducing nounatlas, a large-scale predicate inventory for this task. nounatlas is constructed by linking nominal predicates from wordnet to ver-batlas frames through a semi-automatic approach. we additionally create a large dataset for nominal srl with the help of llms, annotated through label propogation with nominal predicates and semantic roles from nounatlas. finally, we benchmark multiple baselines consisting of state-of-theart srl approaches, trained and evaluated on our dataset. our experiments demonstrate that a unified srl approach, trained on both nominal and verbal data, achieves state-of-the-art performance in both settings. we release our code and resource at https://github.com/sapienzanlp/nounatlas .