# Deep Semantic Parsing with Upper Ontologies
## 6 Discussion
The research team developed a parser that augments PropBank labels with FrameNet, WordNet and SUMO labels. PropBank annotations are less domain-specific and the label set size is relatively small (in our corpus, over 90% of annotations are covered using only 12 labels). On the other hand, the FrameNet label set size exceeds 1000 and WordNet exceeds 100,000. These labels allow us to refine PropBank annotations and prepare parsing results to map natural language labels to domain-specific ontology. The manually annotated corpus shows that it is a sound idea. It is important to notice that this approach allows one to filter out a significant part of the information that is not related to the text-to-3D task. If one takes the first sentence from the proposed example, then this filtering process will look like this: (1) the PropBank parser will take the verb phase \"_was seated_\" and select the word \"_seated_\" as the headword that marks the predicate under consideration; (2) then the PropBank parser will indicate the phrase \"_Mr. [PERSON], who was usually very late in the mornings, save upon those not infrequent occasions when he was up all night_\" as an argument of type <ARG1> and the phrase \"_at the breakfast table_\" as an argument of type <ARG2>, and this will indicate that the system can compress two long phrases into two entities in a 3D scene. The whole process of labeling that was mentioned gives us only shallow semantic information, that is, a system cannot make a logical inference using the axioms of ontology or logical relations expressed as predicates of the ontology. The SUMO ontology has a Sigma browser with built-in automated theorem-proving systems, in particular E and Vampire [31; 32]. It is possible to use these automated theorem-proving systems to reason about implicit knowledge in a scene, but in the proposed framework, the project team decided to test the more programmer-friendly Drools system. The team transformed SUMO ontology concepts into Java classes and SUMO axioms into Drools rules. Many formal systems have been suggested to connect natural language with objects and movements in 3D scenes. The SHRDLU system presented a starting point for this purpose. More recently, the Stanford Text2 Scene system presented a text-to-3D scene generation solution by learning spatial knowledge from 3D scene data. The authors demonstrated that it is possible to infer unstated implicit constraints between various objects in a room scene. This paper focuses on the 3D scene-modeling system's physics engine rigid-body collision detection subsystem. These subsystems are used to simulate the motion of solid objects. They affect the position and orientation of objects and do not deform them. The system uses box-like collision shapes, the simplest possible collision shape of the object, because the physics engine must be simple in our goal to build a natural language processing system that can interact with 3D modeling systems. The research team defined concept-to-physics generation as the task of taking semantic role labels that describe a scene in a book as an input, and generating a plausible 3D scene representation in Drools working memory in terms of Java objects as the output. More specifically, based on the labels from the NLP framework, the system instantiates objects in the [PERSON] memory and then runs an inference process based on the [PERSON] agenda-group workflow. The presented experiments showed that for WordNet sense disambiguation, the system is on par with the prior state of the art for verbs and nouns. Moreover, they showed that by replacing WordNet synsets with a small set of upper ontology concepts, it is possible to improve the accuracy of the identification of predicates. It is possible to improve the performance of word embedding using the noisy text [33] approach. This could be an interesting project for future research on word embeddings.