2016/04/19 by Yijia Liu, Liu, Yijia, Wanxiang Che +7
Computer Science · #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Cartography #Computation and Language (cs.CL) #Computer science #Embedding #FOS: Computer and information sciences #Geography #Market segmentation #Natural Language Processing Techniques #Natural language processing #Pattern recognition (psychology) #Segmentation #Speech Recognition and Synthesis #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1604.05499
arxiv created 2016/04/19 · openalex publication_date 2016/04/19 · arxiv updated 2016/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. Our model represents a segment both by composing the input units and embedding the entire segment. We thoroughly study different composition functions and different segment embeddings. We conduct extensive experiments on two typical segmentation tasks: named entity recognition (NER) and Chinese word segmentation (CWS). Experimental results show that our neural semi-CRF model benefits from representing the entire segment and achieves the state-of-the-art performance on CWS benchmark dataset and competitive results on the CoNLL03 dataset.