# Deep Semantic Parsing with Upper Ontologies
## 4 Integration of PropBank, FrameNet and WordNet
The system uses a mapping from FrameNet to the SUMO ontology to annotate the most frequent FrameNet frames and lexical units with SUMO concept labels. 5. The procedure for finding the embeddings of SUMO concepts is the same as for identifying frames. Figure 7 shows a small experiment that was conducted to demonstrate our approach for word sense disambiguation. Let us return to the first sentence of our example presented in Figure 1 and analyze the word \"_table_\". The system selects from the training corpus all sentences with the word \"_table_\" and calculates the embeddings for each instance of the word \"_table_\". It uses NLTK WordNet sense labels as the class label. Then it computes principal component analysis for 90% of the sentences from the training data and uses the rest as test data. Figure 7a shows all embedded examples for the word \"_table_\" projected on two principal components. It can be seen that there are two clusters of data points. One (brown), marked with the label \"_table.n.01_\", represents a sense with the description: \"_a set of data arranged in rows and column_\". The second largest group of senses is labeled \"table.n.02\" with the description: \"_a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs_\". These two senses can be perfectly separated, and what is more, the system does not need to store all 1024 dimensional embedding vectors in a database. It is enough to have two principal components of the embedding space in order to have a perfect classification of these senses. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**FrameNet** & **PropBank** & **\# of** & **P(_F-role_ 1_ _P-Role_)** \\
**Role** & **Role** & **Cases** & **P(_F-role_ 1_ _P-Role_)** \\ \hline Agent & ARG1 & 83 & 1 \\ Location & ARG2 & 71 & 1 \\ Depictive & ARGM-PRD & 11 & 0.73 \\ Depictive & ARGM-MNR & 5 & 1 \\ Time & ARGM-TMP & 5 & 0.63 \\ Agent & ARGM-R-ARG1 & 4 & 1 \\ Dependent\_state & ARGM-PRD & 4 & 0.27 \\ Depictive & ARGM-ADV & 4 & 1 \\ Duration & ARGM-TMP & 3 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics for the frame <_Posture_> and the verb “_stand_” in the FrameNet corpus after the system has parsed it using the AllenNLP system (P-Role stands for PropBank role and F-role stands for FrameNet role). There is one more important conclusion that can be drawn from Figure 7a using the WordNet dictionary--using hypernym relations, one can conclude that the meaning of \"table.n.05\" (red square) is an abstract thing. The description of this sense is as follows: _\"a group of persons together in one place\"_, and the direct hypernym is _\"social_group?o:1:14:00:_\". Now let us observe which sentence is encoded with this red square: _\"I felt the temblor begin and glanced at the table next to mine, smiled that guilty smile and we both moudhed the words,\"Earth-quake! \"together.\"_ If the system wants to recreate this sentence as a 3D scene, then the word _\"table\"_ would represent a set of objects: people and a table as furniture. On the other hand, WordNet will suggest interpreting it as an abstract object and ignoring it for the scene generation task. There are two important lessons to be drawn from these examples. First, the system needs to use caution when using WordNet for a text-to-3D task, and second, the WSD error from the WordNet point of view does not mean an error in terms of a text-to-3D task. Figure 7b shows another interesting point about the semantic meaning of word embeddings in context. It is shown here that the greatest separation between data points occurs when the system considers things to be divided into abstract and physical categories. Figure 7: Clustering of word embeddings in the WordNet corpus, (**a**) all embedded examples for the word “table”, (**b**) examples for the words: “table”, “lamp”, “bookcase”, “nest”, (**c**) embedded examples for the word “run”, (**d**) senses for the word “run” after labeling them into two groups: “Motion” and “NoMotion”.