Set Of Semantic Roles Research Articles

Deep Semantic Parsing with Upper Ontologies

This paper presents a new method for semantic parsing with upper ontologies using FrameNet annotations and BERT-based sentence context distributed representations. The proposed method leverages WordNet upper ontology mapping and PropBank-style semantic role labeling and it is designed for long text parsing. Given a PropBank, FrameNet and WordNet-labeled corpus, a model is proposed that annotates the set of semantic roles with upper ontology concept names. These annotations are used for the identification of predicates and arguments that are relevant for virtual reality simulators in a 3D world with a built-in physics engine. It is shown that state-of-the-art results can be achieved in relation to semantic role labeling with upper ontology concepts. Additionally, a manually annotated corpus was created using this new method and is presented in this study. It is suggested as a benchmark for future studies relevant to semantic parsing.

ОСОБЕННОСТИ ПРЕДИКАТНО-АРГУМЕНТНОЙ СТРУКТУРЫ КАУЗАТИВНОЙ КОНСТРУКЦИИ HAVE SOMETHING DONE В АНГЛИЙСКОМ ЯЗЫКЕ

The article is devoted to studying the features of the predicate-argument structure of the causative construction “have something done” in the English language. This causative construction has two interpretations - “have your hair cut, your house painted etc.” and “have something stolen, broken, taken etc.”. The paper analyses the argument structure of each of the constructions (a set of semantic roles), determines the type of the taxonomic category of the predicate. The author ascertains the correlation: differences in the communicative content of causative situations and the ratio of arguments lead to changes in the referential properties of the participants of causative situations.

Generalizing the semantic roles in the Chinese Proposition Bank

The Chinese Proposition Bank (CPB) is a corpus annotated with semantic roles for the arguments of verbal and nominalized predicates. The semantic roles for the core arguments are defined in a predicate-specific manner. That is, a set of semantic roles, numerically identified, are defined for each sense of a predicate lemma and recorded in a valency lexicon called frame files. The predicate-specific manner in which the semantic roles are defined reduces the cognitive burden on the annotators since they only need to internalize a few roles at a time and this has contributed to the consistency in annotation. It was also a sensible approach given the contentious issue of how many semantic roles are needed if one were to adopt of set of global semantic roles that apply to all predicates. A downside of this approach, however, is that the predicate-specific roles may not be consistent across predicates, and this inconsistency has a negative impact on training automatic systems. Given the progress that has been made in defining semantic roles in the last decade or so, time is ripe for adopting a set of general semantic roles. In this article, we describe our effort to "re-annotate" the CPB with a set of "global" semantic roles that are predicate-independent and investigate their impact on automatic semantic role labeling systems. When defining these global semantic roles, we strive to make them compatible with a recently published ISO standards on the annotation of semantic roles (ISO 24617-4:2014 SemAF-SR) while taking the linguistic characteristics of the Chinese language into account. We show that in spite of the much larger number of global semantic roles, the accuracy of an off-the-shelf semantic role labeling system retrained on the data re-annotated with global semantic roles is comparable to that trained on the data set with the original predicate-specific semantic roles. We also argue that the re-annotated data set, together with the original data, provides the user with more flexibility when using the corpus.