This paper presents a method for Japanese morphological analysis using Conditional Random Fields (CRFs). Previous approaches using CRFs assumed fixed word boundaries, but Japanese lacks clear word boundaries, making such assumptions invalid. CRFs are shown to handle word boundary ambiguity effectively, addressing long-standing issues in Japanese morphological analysis. CRFs allow flexible feature design for hierarchical tagsets and minimize label and length bias, which are problematic in Hidden Markov Models (HMMs) and Maximum Entropy Markov Models (MEMMs). Experiments on standard Japanese morphological analysis corpora show that CRFs outperform HMMs and MEMMs in accuracy. CRFs are discriminative models that capture correlated features, enabling flexible feature design. They are trained to discriminate the correct sequence from all other candidate sequences without assuming independence of features. Unlike HMMs and MEMMs, CRFs avoid label and length bias, which can lead to errors in decoding. In Japanese morphological analysis, word boundaries are ambiguous, and CRFs can handle this by using a lattice of possible paths, allowing for variable numbers of tokens per path. The paper discusses the challenges of Japanese morphological analysis, including hierarchical tagsets and label/length bias. CRFs are shown to overcome these issues by incorporating a wide range of features and avoiding bias. Experiments on two Japanese corpora (Kyoto University Corpus and RWCP Text Corpus) demonstrate that CRFs outperform HMMs and MEMMs. The results indicate that CRFs are more accurate and robust, especially in handling ambiguous word boundaries. The paper also compares L1-CRFs and L2-CRFs, showing that L2-CRFs perform slightly better, indicating that most features are relevant to both datasets. However, L1-CRFs use fewer features, making them more suitable for resource-constrained environments. The study concludes that CRFs are a promising approach for Japanese morphological analysis and can be applied to other non-segmented languages. Future work includes exploring more complex feature sets and efficient feature selection methods to handle longer contexts.This paper presents a method for Japanese morphological analysis using Conditional Random Fields (CRFs). Previous approaches using CRFs assumed fixed word boundaries, but Japanese lacks clear word boundaries, making such assumptions invalid. CRFs are shown to handle word boundary ambiguity effectively, addressing long-standing issues in Japanese morphological analysis. CRFs allow flexible feature design for hierarchical tagsets and minimize label and length bias, which are problematic in Hidden Markov Models (HMMs) and Maximum Entropy Markov Models (MEMMs). Experiments on standard Japanese morphological analysis corpora show that CRFs outperform HMMs and MEMMs in accuracy. CRFs are discriminative models that capture correlated features, enabling flexible feature design. They are trained to discriminate the correct sequence from all other candidate sequences without assuming independence of features. Unlike HMMs and MEMMs, CRFs avoid label and length bias, which can lead to errors in decoding. In Japanese morphological analysis, word boundaries are ambiguous, and CRFs can handle this by using a lattice of possible paths, allowing for variable numbers of tokens per path. The paper discusses the challenges of Japanese morphological analysis, including hierarchical tagsets and label/length bias. CRFs are shown to overcome these issues by incorporating a wide range of features and avoiding bias. Experiments on two Japanese corpora (Kyoto University Corpus and RWCP Text Corpus) demonstrate that CRFs outperform HMMs and MEMMs. The results indicate that CRFs are more accurate and robust, especially in handling ambiguous word boundaries. The paper also compares L1-CRFs and L2-CRFs, showing that L2-CRFs perform slightly better, indicating that most features are relevant to both datasets. However, L1-CRFs use fewer features, making them more suitable for resource-constrained environments. The study concludes that CRFs are a promising approach for Japanese morphological analysis and can be applied to other non-segmented languages. Future work includes exploring more complex feature sets and efficient feature selection methods to handle longer contexts.