KEYWORDS: Data modeling, Education and training, Transformers, Performance modeling, Semantics, Data processing, Feature extraction, Matrices, Analytical research
Aspect-based sentiment analysis (ABSA) is a crucial granular task within sentiment analysis, focusing on the precise identification of sentiment orientations for specific aspects within text. Recognizing that identical context words can express opposing sentiment polarities in different situations, it's essential to delve into the nuanced interactions between target and context words. This study introduces an RCG-based Hybrid Attention Network, a novel architecture that adeptly utilizes lexical attention mechanisms to extract lexical features and fortify the relationship between aspects and their corresponding target words. To assess the efficacy of our proposed approach, we conducted experiments on a well-known public dataset. The results show a significant 3.37% enhancement in accuracy and a 1.38% improvement in Macro-F1 scores compared to related methods, affirming the superiority of our technique in enhancing the performance of aspect-level sentiment analysis.
Aiming at the problems of lack of emotional resonance and unsatisfactory anthropomorphic effect in the responses generated by the existing dialog models, a dialog generation model based on the prediction of emotional intent is proposed. The model predicts the intention information of the context to obtain fine-grained emotion information to construct a high-quality dialog generation system, combines with an emotion classifier to identify the emotion category of the input utterance, and generates corresponding empathetic replies based on the emotion comparison mechanism according to the different intention information and emotion information. The experimental results show that compared with the traditional dialog generation model, the proposed model can recognize the user's emotional intent and emotion information, generate responses that are more in line with the user's expectations and adapt to the context, and realize a more empathetic emotional dialog process.
Based on the platform of Mat lab, a program was designed to help realize the conversion from Kazak in China to IPA with its mathematical computation and the function of GUI. Thus, analysis, comparison and other researches on Kazak phonetics can be more available without the complicated principles in word spellings and pronunciations. The basic principle for the design of the software is to firstly attribute each word in line with the features such as words with the signal for front vowels, compound words, words affiliated with numbers for types and so on, then separate the word itself from its attributes and divide the compound word into front word and back word, and finally convert Kazak words into words in IPA according to the pronunciation rules and the check list between Kazak letter and IPA.
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