CRI CI-P: Building a Community Resource for Temporal Inference in Chinese

Brandeis University, Waltham MA

Investigators

Abstract

Temporal inference is the task of identifying the location of an event and the ordering of events in a temporal sequence. To perform temporal inference adequately, linguistic information from a variety of sources must be exploited. For example, to determine the temporal location of an event, one needs to look at the time expression (``at ten o'clock") associated with the event, tense (past, present) and aspect (perfective, progressive) markers, and temporal adverbials (``already"). To conduct temporal inference automatically, the dominant research paradigm in the Natural Language Processing (NLP) community is to provide a large corpus of human annotated data for supervised machine learning algorithms, which are capable of combining the divergent sources of linguistic information to infer the temporal location and order of events. Under the planning grant, the PIs are funded to work with the research community to define an annotation methodology for a large-scale Chinese corpus that can support research in multilingual temporal inference in conjunction with existing resources for English. This involves gathering input from the research community on (1) the extension of the existing temporal annotation framework for English to Chinese and possibly to other Asian languages, (2) the methods to bootstrap the temporal annotation automatically through existing linguistic resources. A workshop attended by influential researchers in this area is a central part of this planning grant. Chinese is chosen as the primary language for this project because of its distinctive linguistic properties and the lack of necessary linguistic resources to perform temporal inference in this language, although the methodologies developed in this effort are expected to be readily generalizable to other Asian languages. Temporal inference is a fundamental technology that supports many natural language applications, including but not limited to Information Extraction, Question Answering, Text Summarization and Machine Translation. With the rise of China as a global power, advancing temporal inference technology will enable greater information access in a language that has strategic importance.