Topic recognition technology has been commonly applied to identify different categories of news topics from the vast amount of web information, which has a wide application prospect in the field of online public opinion monitoring, news recommendation, and so on. However, it is very challenging to effectively utilize key feature information such as syntax and semantics in the text to improve topic recognition accuracy. Some researchers proposed to combine the topic model with the word embedding model, whose results had shown that this approach could enrich text representation and benefit natural language processing downstream tasks. However, for the topic recognition problem of news texts, there is currently no standard way of combining topic model and word embedding model. Besides, some existing similar approaches were more complex and did not consider the fusion between topic distribution of different granularity and word embedding information. Therefore, this paper proposes a novel text representation method based on word embedding enhancement and further forms a full-process topic recognition framework for news text. In contrast to traditional topic recognition methods, this framework is designed to use the probabilistic topic model LDA, the word embedding models Word2vec and Glove to fully extract and integrate the topic distribution, semantic knowledge, and syntactic relationship of the text, and then use popular classifiers to automatically recognize the topic categories of news based on the obtained text representation vectors. As a result, the proposed framework can take advantage of the relationship between document and topic and the context information, which improves the expressive ability and reduces the dimensionality. Based on the two benchmark datasets of 20NewsGroup and BBC News, the experimental results verify the effectiveness and superiority of the proposed method based on word embedding enhancement for the news topic recognition problem.

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid.

Compared with traditional computers, quantum computers can provide exponential acceleration for certain critical fields. However, the coupling of quantum systems with the environment, along with the intrinsic characteristics of quantum systems, has collectively introduced quantum noise, which has emerged as a significant impediment to the development of quantum computing. Quantum error mitigation (QEM) has been proposed as an alternative solution in the noisy intermediate-scale quantum era. In recent years, with the rise of artificial intelligence, machine learning-based QEM technology has received attention from the industry. However, the latest machine learning-based QEM techniques have limitations, especially their inability to mitigate errors in the quantum circuits whose number of qubits exceeds the number of qubits in the training set, and their tendency to amplify noise when constructing feature sets. This paper proposes QEMOS, a novel random forest-based machine learning model that utilizes a new feature dataset incorporating quantum computer backend properties, with feature dimensionality reduction enabling decoupling from the number of qubits. The model is trained and tested using six different simulators from Qiskit and a real quantum computer tianyan-176. It is worth noting that this model overcomes the limitation of sensitivity to the number of qubits, which was the main problem of previous methods. When trained on 5–9 qubit circuits, the model achieves a probability of correct mitigation of 86.38% on 2–13 qubit circuits, though this efficacy is observed primarily for circuits exhibiting high-probability outputs and decreases as all output probabilities approach zero. Compared to the baseline, the model demonstrates a 31.74% error reduction on test sets with more qubits than the training set. On real quantum computer, testing shows an average error reduction of 67.5%.