Improvement in Domain-Specific Named Entity Recognition by Utilizing the Real-World Data

Abstract

In this paper, we propose a method that utilizes real-world data to improve named entity recognition (NER) for a particular domain. Our proposed method integrates a stacked auto-encoder (SAE) and a text-based deep neural network for achieving NER. Initially, we train the SAE using real-world data, then the entire deep neural network from sentences annotated with named entities (NEs) and accompanied by real world information. In our experiments, we chose Japanese chess as our subject. The dataset consists of pairs of a game state and commentary sentences about it annotated with game-specific NE tags. We conducted NER experiments and verified that referring to real-world data improves the NER accuracy.