Visualizing Natural Language Descriptions: A Survey <s> Carsim <s> This paper describes a prototype system to visualize and animate 3D scenes from car accident reports, written in French. The problem of generating such a 3D simulation can be divided into two subtasks: the linguistic analysis and the virtual scene generation. As a means of communication between these two modules, we first designed a template formalism to represent a written accident report. The CARSIM system first processes written reports, gathers relevant information, and converts it into a formal description. Then, it creates the corresponding 3D scene and animates the vehicles. <s> BIB001 </s> Visualizing Natural Language Descriptions: A Survey <s> Carsim <s> CarSim is an automatic text-to-scene conversion system. It analyzes written descriptions of car accidents and synthesizes 3D scenes of them. The conversion process consists of two stages. An information extraction module creates a tabular description of the accident and a visual simulator generates and animates the scene.We implemented a first version of CarSim that considered a corpus of texts in French. We redesigned its linguistic modules and its interface and we applied it to texts in English from the National Transportation Safety Board in the United States. <s> BIB002 </s> Visualizing Natural Language Descriptions: A Survey <s> Carsim <s> This paper describes a system to create animated 3D scenes of car accidents from reports written in Swedish. The system has been developed using news reports of varying size and complexity. The text-to-scene conversion process consists of two stages. An information extraction module creates a structured representation of the accident and a visual simulator generates and animates the scene. ::: ::: We first describe the overall structure of the text-to-scene conversion and the structure of the representation. We then explain the information extraction and visualization modules. We show snapshots of the car animation output and we conclude with the results we obtained. <s> BIB003
Carsim BIB002 BIB001 BIB003 ] is a domain-specific system developed for generating simple animations of car accidents based on a set of Swedish accident reports collected from news articles, narratives from victims, and official transcriptions from officers. It consists of two main modules including information extraction module and visualization module. The information extraction module analyzes the input text and converts it to a triplet representation ¡S,R,C¿ in which S denotes scene objects such as weather, R represents a set of road objects such as cars, and C is a set of collisions that happened in the accident. This module utilizes the Granska POS tagger  for tagging the input text and uses a small lexicon and a few regular expressions to extract the named entities, such as street names. It also exploits a local dictionary extracted from WordNet to discover the action verbs. A light domain-specific ontology combined with a classifier that is trained using a small set of example reports are employed to extract the events from textual description of the accidents. The ontology is also utilized to solve the co-references. The visualization module utilizes an animation planner and a graphical engine to render the planned animation. The animation planner exploits a naive greedy algorithm to plan the animation considering the constraints, initial positions, initial directions, and the trajectories. The planning algorithm does not support backtracking and thus cannot find the optimal plans. The constraints are addressed using a small set of spatial and temporal rules. The initial direction and position are inferred directly from the input report and then propagated to those objects whose initial condition is not explicitly mentioned in the report. The trajectories are acquired using the Iterative deepening A * (IDA * ) algorithm. Carsim is a good example of a practical text-to-animation conversion system that mostly focuses on the practical aspects rather than theoretical arguments. It has shown a fair degree of success in its limited domain. Yet it lacks a solid mechanism to harvest the information from user interactions and feedbacks. It also does not contain a strong object repository or lexical resources.