Use of Time-Aware Language Model in Entity Driven Filtering System

Tracking entities, so that new or important information about that entities are caught, is a real challenge and has many applications (e.g., information monitoring, marketing,...). We are interesting in how to represent an entity profile to fulfill two purposes: 1. entity detection and disambiguation, 2. novelty and importance quantification. We propose an entity profile, which uses two language models. First, the Reference Language Model (RLM), which is mainly used for disambiguation. Second, we propose a formalization of a Time-Aware Language Model, which is used for novelty detection. To rank documents, we propose a semi-supervised classification approach which uses meta-features computed on documents using entity profiles and time series.