Error Checking for Chinese Query by Mining Web Log

For the search engine, error-input query is a common phenomenon. This paper uses web log as the training set for the query error checking. Through the n-gram language model that is trained by web log, the queries are analyzed and checked. Some features including query words and their number are introduced into the model. At the same time data smoothing algorithm is used to solve data sparseness problem. It will improve the overall accuracy of the n-gram model. The experimental results show that it is effective

FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering

Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenarios where high-quality annotated data is insufficient. To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing Large Language Models (LLMs) as program translators for addressing the challenges inherent in the few-shot KBQA task. Specifically, FlexKBQA leverages automated algorithms to sample diverse programs, such as SPARQL queries, from the knowledge base, which are subsequently converted into natural language questions via LLMs. This synthetic dataset facilitates training a specialized lightweight model for the KB. Additionally, to reduce the barriers of distribution shift between synthetic data and real user questions, FlexKBQA introduces an executionguided self-training method to iterative leverage unlabeled user questions. Furthermore, we explore harnessing the inherent reasoning capability of LLMs to enhance the entire framework. Consequently, FlexKBQA delivers substantial flexibility, encompassing data annotation, deployment, and being domain agnostic. Through extensive experiments on GrailQA, WebQSP, and KQA Pro, we observe that under the few-shot even the more challenging zero-shot scenarios, FlexKBQA achieves impressive results with a few annotations, surpassing all previous baselines and even approaching the performance of supervised models, achieving a remarkable 93% performance relative to the fully-supervised models. We posit that FlexKBQA represents a significant advancement towards exploring better integration of large and lightweight models. The code is open-sourced.Comment: Accepted as AAAI-24 Oral paper; Knowledge Base Question Answering; Large Language Model; Data Generation; Few-Shot & Zero-Sho

Visualization of Endolymphatic Hydrops in Patients With Unilateral Idiopathic Sudden Sensorineural Hearing Loss With Four Types According to Chinese Criterion

Objective: The aim of this study is to evaluate the possible value of endolymphatic hydrops (EH) in patients with unilateral idiopathic sudden sensorineural hearing loss (UISSNHL) with four types according to audiometry.Methods: Seventy-two patients (40 men and 32 women; age range, 28–78 years; mean age: 50.0 ± 12.9 years) with UISSNHL were admitted retrospectively into this study. Based on the pure tone audiometry before treatment, the hearing loss of all these patients were categorized into four types: low-frequency group (LF-G), high-frequency group (HF-G), flat group (F-G), and total deafness group (TD-G). The average time from symptom onset to the first examination was 6.9 ± 4.4 days (1–20 days). 3D-FLAIR MRI was performed 24 h after intratympanic injection of gadolinium (Gd) within 1 week after the UISSNHL onset. The incidence of EH in the affected ears based on four types of hearing loss were analyzed using the Chi-square test, and the possible relationship with vertigo and prognosis were also assessed.Results: Eleven of 21 patients (52.4%) in LF-G had the highest EH-positive rate, followed by 18.2% in HF-G, 11.8% in F-G, and 17.4% in TD-G. The significant difference was found in the four groups (P = 0.018). The EH rate of LF-G was statistically significantly higher than that of F-G and TD-G (P = 0.009, P =0.014), respectively. After being valued by the volume-referencing grading system (VR scores), the EH level was represented by the sum scores of EH. In LF-G, no statistically significant difference was found in the prognosis of ISSNHL patients between with the EH group and the no EH group (P = 0.586). The symptom “vertigo” did not correlate with EH and prognosis.Conclusions: EH was observed in UISSNHL patients by 3D-FLAIR MRI. EH may be responsible for the pathology of LF-G but not related to prognosis. It might be meaningless to assess EH in other hearing loss types, which might be more related to the blood-labyrinth dysfunction

Mink is a highly susceptible host species to circulating human and avian influenza viruses

Pandemic influenza, typically caused by reassortment of human and avian influenza viruses, can result in severe or fatal infections in humans. Timely identification of potential pandemic viruses must be a priority in influenza virus surveillance. However, the range of host species responsible for the generation of novel pandemic influenza viruses remain unclear. In this study, we conducted serological surveys for avian and human influenza virus infections in farmed mink and determined the susceptibility of mink to prevailing avian and human virus subtypes. The results showed that farmed mink were commonly infected with human (H3N2 and H1N1/pdm) and avian (H7N9, H5N6, and H9N2) influenza A viruses. Correlational analysis indicated that transmission of human influenza viruses occurred from humans to mink, and that feed source was a probable route of avian influenza virus transmission to farmed mink. Animal experiments showed that mink were susceptible and permissive to circulating avian and human influenza viruses, and that human influenza viruses (H3N2 and H1N1/pdm), but not avian viruses, were capable of aerosol transmission among mink. These results indicate that farmed mink could be highly permissive “mixing vessels” for the reassortment of circulating human and avian influenza viruses. Therefore, to reduce the risk of emergence of novel pandemic viruses, feeding mink with raw poultry by-products should not be permitted, and epidemiological surveillance of influenza viruses in mink farms should be urgently implemented

Error Checking for Chinese Query by Mining Web Log

For the search engine, error-input query is a common phenomenon. This paper uses web log as the training set for the query error checking. Through the n-gram language model that is trained by web log, the queries are analyzed and checked. Some features including query words and their number are introduced into the model. At the same time data smoothing algorithm is used to solve data sparseness problem. It will improve the overall accuracy of the n-gram model. The experimental results show that it is effective