Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source.
Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club
