Logs, being run-time information automatically generated by software, record system events and activities with their timestamps. Before obtaining more insights about the run-time status of the software, a fundamental step of log analysis, called log parsing, is employed to extract structured templates and parameters from the semi-structured raw log messages. However, current log parsers regard each message as a character string, ignoring the semantic information included in parameters and templates.Thus, we propose the semantic parser SemParser to unlock the critical bottleneck of mining semantics from log messages. It contains two steps, an end-to-end semantic miner and a joint parser. Specifically, the first step aims to identify explicit semantics inside a single log, and the second step is responsible for jointly inferring implicit semantics and computing structural outputs based on the contextual knowledge base. To analyze the effectiveness of our semantic parser, we first demonstrate that it can derive rich semantics from log messages collected from seven widely-applied systems with an average F1 score of 0.987. Then, we conduct two representative downstream tasks, showing that current downstream techniques improve their performance with appropriately extracted semantics by 11.7% and 8.65% in anomaly detection and failure diagnosis tasks, respectively. We believe these findings provide insights into semantically understanding log messages for the log analysis community.
Emotional classification is the process of analyzing and reasoning subjective texts with emotional color, that is, analyzing whether their emotional tendencies are positive or negative. Aiming at the problems of massive data and nonstandard words in the existing Chinese short text emotion classification algorithm, the traditional BERT model does not distinguish the semantics of words with the same sentence pattern clearly, the multi-level transformer training is slow, time-consuming, and requires high energy consumption, this paper proposes to classify users' emotions based on BERT-RCNN-ATT model, and extract text features in depth using RCNN combined with attention mechanism, Multi task learning is used to improve the accuracy and generalization ability of model classification. The experimental results show that the proposed model can more accurately understand and convey semantic information than the traditional model. The test results show that compared with the traditional CNN, LSTM, GRU models, the accuracy of text emotion recognition is improved by at least 4.558%, the recall rate is increased by more than 5.69%, and the F1 value is increased by more than 5.324%, which is conducive to the sustainable development of emotion intelligence combining Chinese emotion classification with AI technology.
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