Visualizing Natural Language Descriptions: A Survey <s> ScriptViz <s> There are two main topics in this thesis, a corpus-based parser and a study of sublanguage. ::: A novel approach to corpus-based parsing is proposed. In this framework, a probabilistic grammar is constructed whose rules are partial trees from a syntactically-bracketed corpus. The distinctive feature is that the partial trees are multi-layered. In other words, only a small number of non-terminals are used to cut the initial trees; other grammatical nodes are embedded into the partial trees, and hence into the grammar rules. Good parsing performance was obtained, even with small training corpora. Several techniques were developed to improve the parser's accuracy, including in particular two methods for incorporating lexical information. One method uses probabilities of binary lexical dependencies; the other directly lexicalizes the grammar rules. Because the grammar rules are long, the number of rules is huge--more than thirty thousand from a corpus of one million words. A parsing algorithm which can efficiently handle such a large grammar is described. A Japanese parser based on the same idea was also developed. ::: Corpus-based sublanguage studies were conducted to relate the notion of sublanguage to lexical and syntactic properties of a text. A statistical method based on word frequencies was developed to define sublanguages within a collection of documents; this method was evaluated by identifying the sublanguage of new documents. Relative frequencies of different syntactic structures were used to assess the domain dependency of syntactic structure in a multi-domain corpus. Cross-entropy measurements showed a clear distinction between fiction and non-fiction domains. Experiments were then performed in which grammars trained on individual domains, or sets of domains, were used to parse texts in the same or other domains. The results correlate with the measurements of syntactic variation across domains; in particular, the best performance is achieved using grammars trained on the same or similar domains. ::: The parsing and sublanguage techniques were applied to speech recognition. Sublanguage techniques were able to increase recognition accuracy, and some promising cases were found where the parser was able to correct recognition errors. <s> BIB001 </s> Visualizing Natural Language Descriptions: A Survey <s> ScriptViz <s> We have built the first version of ScriptVis v.1.0 that allows users to visualize their screenplays in real time via animated graphics. Our system consists of a text understanding module, a high-level planning module and a scene generator. The user can input a screenplay as a set of well-formed English sentences via a graphic user interface. The text understanding module interprets the input sentences and triggers the high-level planner to construct a plan of actions for the appropriate agents. Then the agents execute the plans, and the scene generator renders the scene as the story evolves. Our system provides the user with a powerful tool to visualize his screenplays (stories) in the form of computer graphics, which makes writing story fun for students as well as for screenplay writers. <s> BIB002
ScriptViz BIB002 aims to replace the manual storyboard drawing with automatic dynamic scene generation in the motion-picture production process. It is capable of analyzing the screenplays written in well-formed sentences (i.e., grammatically correct and not ambiguous) and animating the corresponding objects, agents, and actions. The system consists of three interacting modules, including a language understanding module, a 14 high-level planner, and a scene generator. The language understanding module uses Apple Pie parser BIB001 to derive the syntactical structure of the input text. It separates the clauses based on the conjunctions, extracts the actions from verbs, and recognizes the objects from proper nouns. The verb and proper noun are matched against the actions and the objects using a naive binary matching mechanism, respectively. The high-level planning module generates action plans based on the information received from the language understanding module. The planning process is completed within four consecutive phases. First, an offline plan outline is extracted from a plan database in respect to the objects and actions detected in the input script. The states of the objects and agents are then collected from the virtual environment. This information is used to decide the feasibility of actions according to the current configuration of the environment. In case of a feasible action, parameters of the offline plan are set and the result is represented using PAR structure  . The scene generator assigns the resulted PAR to the corresponding agent, updates the states, and renders the scene in real time. ScriptViz is implemented in Java and uses OpenGL as its graphical engine. It does not support interactive modification of the generated animation and does not embed any lexical or common-sense resources. Also, it has a very limited model repository and scene layout options. These limitations result in weak visualizations of given scripts. Furthermore, it is not clear what kind of actions the agents can perform as the treatment of articulated bodies is not discussed in this work.