Semantic Class Labeling Based on CRF for Limited Domain Searching Service

Abstract

In the process of spoken language understanding (SLU), the first step is to label semantic concept (or class). Since there are variations induced by automatic speech recognition (ASR) or missing field of keywords in limited domain, extracting the main semantic concepts will be the key problem in SLU. In this paper, we utilize some effective features based on conditional random field (CRF) to segment Chinese named entities, which include people's name and other names in domain. The goal of this paper is to acquire the key semantic concept regardless of whether it is a complete or fragment concept. Experiments show that the whole of performance of named entities recognition arrives at 96.67%.