The area of designing semantic/meaning representations is a dynamic one with new formalisms and extensions being proposed continuously. It may be challenging for users of semantic representations to select the relevant formalism for their purpose or for newcomers to the field to select the features they want to represent in a new formalism. In this paper, we propose a set of structural and global features to consider when designing formalisms, and against which formalisms can be compared. We also propose a sample comparison of a number of existing formalisms across the selected features, complemented by a more entailment-oriented comparison on the phenomena of the FraCaS corpus.

A number of graph-based semantic representation frameworks have emerged in recent years, but there are few parallel annotated corpora across them. We want to explore the viability of transforming graphs from one framework into another to construct parallel datasets. In this work, we consider graph rewriting from Discourse Representation Structures (Parallel Meaning Bank (PMB) variant) to Abstract Meaning Representation (AMR). We first build a gold AMR corpus of 102 sentences from the PMB. We then construct a rule base, aided by a further 95 sentences. No benchmark for this task exists, so we compare our system’s output to that of state-of-the-art AMR parsers, and explore the more challenging cases. Finally, we discuss where the two frameworks diverge in encoding semantic phenomena.

This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework. After a break, caused by the Covid-19 pandemic, the project was relaunched in early 2022 with a second working draft of an annotation scheme, which is discussed in this paper. Keywords: semantic annotation, quantification, interoperability, annotation schema, ISO standard

This paper presents how the online tool Grew-match can be used to make queries and visualise data from existing semantically annotated corpora. A dedicated syntax is available to construct simple to complex queries and execute them against a corpus. Such queries give transverse views of the annotated data, this views can help for checking the consistency of annotations in one corpus or across several corpora. Grew-match can then be seen as an error mining tool: when inconsistencies are detected, it helps finding the sentences which should be fixed. Finally, Grew-match can also be used as a side tool to assist annotation task helping to find annotations examples in existing corpora to be compare to the data to be annotated.

In this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR) - a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA) - an anchored framework. We use a corpus-based approach to build two graph rewriting systems, a deterministic and a non-deterministic one, from the former to the latter framework. We present their evaluation and a number of ambiguities that we discovered while building our rules. Finally, we provide a discussion and some future work directions in relation to comparing semantic frameworks of different flavors.