Abstract
Although most Natural Language Processing tasks, such as Text Classification and Natural Language Translation, have experienced a major performance improvement due to recent advances in neural network architectures, Temporal Relation Extraction remains an open challenge. This leaves the door open for new research questions. In this paper, we provide a brief summary of the task and some of the recent efforts that have been made to solve it. In addition, some research opportunities yet to be explored are also discussed.

Abstract
Events like Valentine's Day and Christmas can influence user intent when interacting with search engines. For example, a user searching for gift around Valentine's Day is likely to be looking for Valentine's-themed options, whereas the same query close to Christmas would more likely suggest an interest in Holiday-themed gifts. These shifts in user intent, driven by temporal factors, are often implicit but important to determine the relevance of search results. In this demo, we explore how incorporating temporal awareness can enhance search relevance in an e-commerce setting. We constructed a database of 2K events and, using historical purchase data, developed a temporal model that estimates each event's importance on a specific date. The most relevant events on the date the query was issued are then used to enrich search results with event-specific items. Our demo illustrates how this approach enables a search system to better adapt to temporal nuances, ultimately delivering more contextually relevant products.