The distributional hypothesis, which states that words that occur in similar contexts tend to have similar meanings, has inspired sev- eral Web mining algorithms for paraphras- ing semantically equivalent phrases. Unfortu- nately, these methods have several drawbacks, such as confusing synonyms with antonyms and causes with effects. This paper intro- duces three Temporal Correspondence Heuris- tics, that characterize regularities in parallel news streams, and shows how they may be used to generate high precision paraphrases for event relations. We encode the heuristics in a probabilistic graphical model to create the NEWSSPIKE algorithm for mining news streams. We present experiments demon- strating that NEWSSPIKE significantly outper- forms several competitive baselines. In order to spur ...

Click Here to View the Paper In XML Format