Kronolojik terim ağırlıklandırması yöntemiyle yeni olay bulma

News web pages are an important resource for news consumers since the Internetprovides the most up-to-date information. However, the abundance of this informationis overwhelming. In order to solve this problem, news articles should be organized invarious ways. For example, new event detection (NED) and tracking studies aim tosolve this problem by categorizing news stories according to events. Generally,important issues are presented at the beginning of news articles. Based on thisobservation, we modify the term weighting component of the Okapi similarity measurein several different ways and use them in NED. We perform numerous experiments inTurkish using the BilCol2005 test collection that contains 209,305 documents from theentire year of 2005 and involves several events in which eighty of them are annotated byhumans. In this study, we developed various chronological term ranking (CTR)functions using term positions with several parameters. Our experimental results showthat CTR in combination with Okapi improves the effectiveness of a baseline systemwith a desirable performance up to 13%. We demonstrate that NED using CTR has arobust performance in different versions of TDT collection generated by N-passdetection evaluation. The tests indicate that the improvements are statisticallysignificant.