CEREC: A Corpus for Entity Resolution in Email Conversations

Abstract

We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 38,996 entity coreference chains. The annotation is carried out as a two-step process with minimal manual effort. Experiments are carried out for evaluating different features and performance of four baselines on the created corpus. For the task of mention identification and coreference resolution, a best performance of 54.1 F1 is reported, highlighting the room for improvement. An in-depth qualitative and quantitative error analysis is presented to understand the limitations of the baselines considered.

Meliorating usable document density for online event detection

“…Hence, highlighting the importance of social media streams in event detection, as a result, SNS gained popularity as a perfect source of event detection. Recent works such as Dakle et al [9] on co-referential methodology performed on emails related text, Wright-Bettner et al [10] on cross-document co-reference, where the goal of authors in [11] specifically on mission-related objects, locations, and actors, therefore, annotate a dataset of reference links, with the inclusion of coreferences.…”