Named entity normalization in user generated content

Named entity recognition is important for semantically oriented retrieval tasks, such as question answering, entity retrieval, biomedical retrieval, trend detection, and event and entity tracking. In many of these tasks it is important to be able to accurately normalize the recognized entities, i.e., to map surface forms to unambiguous references to real world entities. Within the context of structured databases, this task (known as record linkage and data de-duplication) has been a topic of active research for more than five decades. For edited content, such as news articles, the named entity normalization (NEN) task is one that has recently attracted considerable attention. We consider the task in the challenging context of user generated content (UGC), where it forms a key ingredient of tracking and media-analysis systems. A baseline NEN system from the literature (that normalizes surface forms to Wikipedia pages) performs considerably worse on UGC than on edited news: accuracy dro...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Valentin Jijkoun, Mahboob Alam Khalid, Maarten Marx, Maarten de Rijke
• Keywords: Baseline Nen, Entity Recognition, Information Technology, NEN Algorithm, SIGIR 2008