This paper explores the application of conditional random fields (CRFs) to Japanese morphological analysis, addressing the challenge of word boundary ambiguity in non-segmented languages. Traditional methods like Hidden Markov Models (HMMs) and Maximum Entropy Markov Models (MEMMs) struggle with this issue, leading to problems such as label bias and length bias. CRFs, however, offer a flexible solution by allowing the inclusion of various features, including hierarchical tagsets and non-independent features, while minimizing the influence of label and length bias. The authors propose a novel formulation of CRFs tailored for Japanese morphological analysis, which uses a lattice to represent all possible paths and tokens. They evaluate their approach on two standard Japanese corpora, the Kyoto University Corpus ver 2.0 (KC) and RWCP Text Corpus, and compare it with HMMs and MEMMs. The results show that CRFs outperform other methods, demonstrating their effectiveness in improving the accuracy of Japanese morphological analysis. The paper also discusses the advantages of using L1 and L2 regularization in CRFs and highlights the potential for extending CRFs to handle longer contexts using n-gram features.This paper explores the application of conditional random fields (CRFs) to Japanese morphological analysis, addressing the challenge of word boundary ambiguity in non-segmented languages. Traditional methods like Hidden Markov Models (HMMs) and Maximum Entropy Markov Models (MEMMs) struggle with this issue, leading to problems such as label bias and length bias. CRFs, however, offer a flexible solution by allowing the inclusion of various features, including hierarchical tagsets and non-independent features, while minimizing the influence of label and length bias. The authors propose a novel formulation of CRFs tailored for Japanese morphological analysis, which uses a lattice to represent all possible paths and tokens. They evaluate their approach on two standard Japanese corpora, the Kyoto University Corpus ver 2.0 (KC) and RWCP Text Corpus, and compare it with HMMs and MEMMs. The results show that CRFs outperform other methods, demonstrating their effectiveness in improving the accuracy of Japanese morphological analysis. The paper also discusses the advantages of using L1 and L2 regularization in CRFs and highlights the potential for extending CRFs to handle longer contexts using n-gram features.