This paper describes a domain-independent, machine-learning based approach to temporally anchoring and ordering events in news. The approach achieves 84.6% accuracy in temporally ...
Many applications in NLP, such as questionanswering and summarization, either require or would greatly benefit from the knowledge of when an event occurred. Creating an effective ...
The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering f...
We propose a method of deriving chronological order of events in natural language texts by constraining temporal boundaries associated to events and projecting them on a timeline....
Models of bags of words typically assume topic mixing so that the words in a single bag come from a limited number of topics. We show here that many sets of bag of words exhibit a...