FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198518797> ?p ?o ?g. }

• W3198518797 endingPage "12" @default.
• W3198518797 startingPage "1" @default.
• W3198518797 abstract "Based on locness corpus, this paper uses Wordsmith 6.0, SPSS 24, and other software to explore the use of temporal connectives in Japanese writing by Chinese Japanese learners. This paper proposes a method of tense classification based on the Japanese dependency structure. This method analyzes the results of the syntactic analysis of Japanese dependence and combines the tense characteristics of the target language to extract tense-related information and construct a maximum entropy tense classification model. The model can effectively identify the tense, and its classification accuracy shows the effectiveness of the classification method. This paper proposes a temporal feature extraction algorithm oriented to the hierarchical phrase expression model. The end-to-end speech recognition system has become the development trend of large-scale continuous speech recognition because of its simplicity and efficiency. In this paper, the end-to-end technology based on link timing classification is applied to Japanese speech recognition. Taking into account the characteristics of Japanese hiragana, katakana, and Japanese kanji writing forms, through experiments on the Japanese data set, different suggestions are explored. The final effect is better than mainstream speech recognition systems based on hidden Markov models and two-way long and short-term memory networks. This algorithm can extract the temporal characteristics of rules that meet certain conditions while extracting expression rules. These tense characteristics can guide the selection of rules in the expression process, make the expression results more in line with linguistic knowledge, and ensure the choice of relevant vocabulary and the structural ordering of the language. Through the analysis of time series and static information, we combine the time and space dimensions of the network structure. Using connectionist temporal classification (CTC) technology, an end-to-end speech recognition method for pronunciation error detection and diagnosis tasks is established. This method does not require phonemic information nor does it require forced alignment. The extended initials and finals are the error primitives, and 64 types of errors are designed. The experimental results show that the method can effectively detect the wrong pronunciation, the detection accuracy rate is 87.07%, the false rejection rate is 7.83%, and the error rate is 87.07%. The acceptance rate is 25.97%. This method uses network information more comprehensively than traditional methods, and the model is more effective. After detailed experiments, this article evaluates the prediction effect of this method and previous methods on the data set. This method improves the prediction accuracy by about 15% and achieves the expected goal of the work in this paper." @default.
• W3198518797 created "2021-09-13" @default.
• W3198518797 creator A5004004747 @default.
• W3198518797 creator A5035094559 @default.
• W3198518797 date "2021-09-04" @default.
• W3198518797 modified "2023-10-14" @default.
• W3198518797 title "Analysis of Japanese Expressions and Semantics Based on Link Sequence Classification" @default.
• W3198518797 cites W1950689016 @default.
• W3198518797 cites W1964135532 @default.
• W3198518797 cites W2019283755 @default.
• W3198518797 cites W2038093509 @default.
• W3198518797 cites W2053805959 @default.
• W3198518797 cites W2054823774 @default.
• W3198518797 cites W2059878748 @default.
• W3198518797 cites W2063939585 @default.
• W3198518797 cites W2088588558 @default.
• W3198518797 cites W2122842976 @default.
• W3198518797 cites W2154716422 @default.
• W3198518797 cites W2750065334 @default.
• W3198518797 cites W2972816644 @default.
• W3198518797 cites W3000032106 @default.
• W3198518797 cites W3030179783 @default.
• W3198518797 cites W3089499347 @default.
• W3198518797 cites W3089593707 @default.
• W3198518797 cites W3104554768 @default.
• W3198518797 cites W3161839756 @default.
• W3198518797 cites W3165517516 @default.
• W3198518797 cites W3189110740 @default.
• W3198518797 cites W8723400 @default.
• W3198518797 doi "https://doi.org/10.1155/2021/3389643" @default.
• W3198518797 hasPublicationYear "2021" @default.
• W3198518797 type Work @default.
• W3198518797 sameAs 3198518797 @default.
• W3198518797 citedByCount "0" @default.
• W3198518797 crossrefType "journal-article" @default.
• W3198518797 hasAuthorship W3198518797A5004004747 @default.
• W3198518797 hasAuthorship W3198518797A5035094559 @default.
• W3198518797 hasBestOaLocation W31985187971 @default.
• W3198518797 hasConcept C154945302 @default.
• W3198518797 hasConcept C199360897 @default.
• W3198518797 hasConcept C204321447 @default.
• W3198518797 hasConcept C23224414 @default.
• W3198518797 hasConcept C2776224158 @default.
• W3198518797 hasConcept C2781051154 @default.
• W3198518797 hasConcept C28490314 @default.
• W3198518797 hasConcept C41008148 @default.
• W3198518797 hasConcept C83535845 @default.
• W3198518797 hasConcept C90559484 @default.
• W3198518797 hasConceptScore W3198518797C154945302 @default.
• W3198518797 hasConceptScore W3198518797C199360897 @default.
• W3198518797 hasConceptScore W3198518797C204321447 @default.
• W3198518797 hasConceptScore W3198518797C23224414 @default.
• W3198518797 hasConceptScore W3198518797C2776224158 @default.
• W3198518797 hasConceptScore W3198518797C2781051154 @default.
• W3198518797 hasConceptScore W3198518797C28490314 @default.
• W3198518797 hasConceptScore W3198518797C41008148 @default.
• W3198518797 hasConceptScore W3198518797C83535845 @default.
• W3198518797 hasConceptScore W3198518797C90559484 @default.
• W3198518797 hasLocation W31985187971 @default.
• W3198518797 hasLocation W31985187972 @default.
• W3198518797 hasOpenAccess W3198518797 @default.
• W3198518797 hasPrimaryLocation W31985187971 @default.
• W3198518797 hasRelatedWork W1615515845 @default.
• W3198518797 hasRelatedWork W1808132108 @default.
• W3198518797 hasRelatedWork W1987783679 @default.
• W3198518797 hasRelatedWork W2043920657 @default.
• W3198518797 hasRelatedWork W2113935282 @default.
• W3198518797 hasRelatedWork W2117295508 @default.
• W3198518797 hasRelatedWork W2133273642 @default.
• W3198518797 hasRelatedWork W2369308426 @default.
• W3198518797 hasRelatedWork W2746789073 @default.
• W3198518797 hasRelatedWork W3146708532 @default.
• W3198518797 hasVolume "2021" @default.
• W3198518797 isParatext "false" @default.
• W3198518797 isRetracted "false" @default.
• W3198518797 magId "3198518797" @default.
• W3198518797 workType "article" @default.