S em L ink 2.0: Chasing Lexical Resources (2021)

Abstract: The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses. To take advantage of these differences, the ability to move between resources is essential. This work describes advances made to improve the usability of the SemLink resource: the automatic addition of new instances and mappings, manual corrections, sense-based vectors and collocation information, and architecture built to automatically update the resource when versions of the underlying resources change. These updates improve coverage, provide new tools to leverage the capabilities of these resources, and facilitate seamless updates, ensuring the consistency and applicability of these mappings in the future.

## introduction hand-crafted lexical resources remain an important factor in natural language processing research, as they can offer linguistic insights that are currently not captured even by modern deep learning techniques. semlink is a connecting point between a number of different lexical semantic resources, providing mappings between different word senses and semantic roles, as well as a corpus of annotation @xcite . semlink has a variety of applications, from performing linguistic analysis of its component parts and their relations @xcite , extracting thematic role hierarchies @xcite , probing of linguistic formalisms @xcite , and computational methods for automatic extraction, improvement, and classification of computational lexical resources @xcite @xcite ). semlink provides a bridge between these resources, allowing users to take advantage of their different features and strengths. for example, the mappings between the semantic role labels allow users to accurately convert annotations done with pb roles to vn roles and combine their respective data sets into a much larger corpus of training and test data. the goal of semlink is to link senses between resources, maximizing the effectiveness of each. it is composed of two primary assets: mappings between resources, and a corpus of annotated instances. these are verbs in context that receive a pb roleset annotation, and vn class tag, a fn frame tag, and a sense tag based on the on groupings. the problem we address here is the constantly changing nature of these resources. they are evolving: new versions incorporate new semantics, new senses, better lexical coverage, and more consistent formatting. this makes it difficult to provide static links between them. semlink has seen previous updates @xcite that improve consistency, but since that time many of the resources it links have undergone significant overhauls. our work updates semlink via four distinct contributions: ## resources a brief description of each resource in semlink follows, along with the changes in each that have been implemented since the previous update. ## improvements and additions semlink incorporates these resources via mapping files (for pb, vn, and fn) and predicate instance annotations (including all four resources). we will now overview each of these artifacts, highlighting the updates in our new release and the tools and practices used to generate these updates. ## conclusions and future work our updates to semlink consist of four main components. (1) we update semlink data to match the current versions of each resource through automatic and manual methods. (2) we add annotations to improve the coverage of the resource. (3) we add sense embeddings and argument information. (4) we provide automatic tools to allow the sem-link resource to be consistently updated. as these lexical resources are always changing, these tools are necessary for the resource to remain viable, and while the process of linking semantic resources can likely never be fully automated, these tools can assist in this process. this work then comes with two artifacts: the new semlink resource (mapping files and annotations) as well as architecture for updating and managing semlink. the coverage is by no means complete and many lexical items in each resource contain no viable mappings. manual annotation of links between resources is essential for the success of the sem-link resource: while we can automatically filter out inaccurate mappings when resources change, this leaves blind spots where we have incomplete mappings, and manual annotation is currently the most accurate way to cover these gaps. another direction of future work is evaluating the usefulness of these linked resources. while there have been evaluations comparing the three semantic role labelling frameworks provided via pb, vn, and fn @xcite , a fullscale evaluation of the links between them is yet to be done, and may provide valuable insight not only into how to best improve semlink, but also into how these kinds of linked resources can be best employed. while modern nlp focuses largely around end-to-end models that implicitly capture semantic relations, there is still a role for handcurated lexical resources to play, and we believe semlink can be an effective resource for those studying computational lexical semantics, word sense disambiguation and semantic role labelling, and other tasks requiring linked lexical resources.