Automatic Content Extraction

Abstract: The objective of the ACE program is to develop technology to automatically infer from human language data the entities being mentioned, the relations among these entities that are directly expressed, and the events in which these entities participate. Data sources include audio and image data in addition to pure text, and Arabic and Chinese in addition to English. The effort involves defining the research tasks in detail, collecting and annotating data needed for training, development, and evaluation, and supporting the research with evaluation tools and research workshops. This program began with a pilot study in 1999. The next evaluation is scheduled for September 2004. Introduction and Background Today’s global web of electronic information, including most notably the www, provides a resource of unbounded information-bearing potential. But to fully exploit this potential requires the ability to extract content from human language automatically. That is the objective of the ACE program – to develop the capability to extract meaning from multimedia sources. These sources include text, audio and image data. The ACE program is a “technocentric” research effort, meaning that the emphasis is on developing core enabling technologies rather than solving the application needs that motivate the research. The program began in 1999 with a study intended to identify those key content extraction tasks to serve as the research targets for the remainder of the program. These tasks were identified in general as the extraction of the entities, relations and events being discussed in the language. In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it (NIST 1999). The ACE program, however, attempts to take the task “off the page” in the sense that the research objectives are defined in terms of the target objects (i.e., the entities, the relations, and the events) rather than in terms of the words in the text. For example, the so-called “named entity” task, as defined in MUC, is to identify those words (on the page) that are names of entities. In ACE, on the other hand, the corresponding task is to identify the entity so named. This is a different task, one that is more abstract and that involves inference more explicitly in producing an answer. In a real sense, the task is to detect things that “aren’t there”. Reference resolution thus becomes an integral and critical part of solving the problem. During the period 2000-2001, the ACE effort was devoted solely to entity detection and tracking. During the period 2002-2003, relations were explored and added. 1 While the ACE program is directed toward extraction of information from audio and image sources in addition to pure text, the research effort is restricted to information extraction from text. The actual transduction of audio and image data into text is not part of the ACE research effort, although the processing of ASR and OCR output from such transducers is. Now, starting in 2004, events are being explored and added as the third of the three original tasks.