Fixed versus Dynamic Co-Occurrence Windows in TextRank Term Weights for Information Retrieval

TextRank is a variant of PageRank typically used in graphs that represent documents, and where vertices denote terms and edges denote relations between terms. Quite often the relation between terms is simple term co-occurrence within a fixed window of k terms. The output of TextRank when applied iteratively is a score for each vertex, i.e. a term weight, that can be used for information retrieval (IR) just like conventional term frequency based term weights. So far, when computing TextRank term weights over co- occurrence graphs, the window of term co-occurrence is al- ways ?xed. This work departs from this, and considers dy- namically adjusted windows of term co-occurrence that fol- low the document structure on a sentence- and paragraph- level. The resulting TextRank term weights are used in a ranking function that re-ranks 1000 initially returned search results in order to improve the precision of the ranking. Ex- periments with two IR collections show that adjusting the vicinity of term co-occurrence when computing TextRank term weights can lead to gains in early precision

Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources, due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem. Aiming to create a "PageRank" for the LLM era, we strive to transform LLM into a relevant, responsible, and trustworthy searcher. We propose a novel generative retrieval framework leveraging the knowledge of LLMs to foster a direct link between queries and online sources. This framework consists of three core modules: Generator, Validator, and Optimizer, each focusing on generating trustworthy online sources, verifying source reliability, and refining unreliable sources, respectively. Extensive experiments and evaluations highlight our method's superior relevance, responsibility, and trustfulness against various SOTA methods.Comment: 14 pages, 4 figures, under peer revie

Isolation and identification of Tete virus group (Peribunyaviridae: Orthobunyavirus) from Culicoides biting midges collected in Lichuan County, China

In July 2018, a virus (JXLC1806-2) was isolated from Culicoides biting midges collected in Lichuan County, Jiangxi Province, China. The virus isolate showed significant cytopathic effects within 48 hours after inoculation with mammalian cells (BHK-21). JXLC1806-2 virus could form plaques in BHK-21 cells, and the virus titer was 1×105.6 pfu/mL. After inoculation with the virus, suckling mice developed disease and died. The nucleotide and amino sequence analysis showed that the JXLC1806-2 virus genome was composed of S, M and L segments. Phylogenetic analysis showed that the S, M and L genes of JXLC1806-2 virus belonged to the Tete serogroup, Orthobunyavirus, but formed an independent evolutionary branch from the other members of the Tete serogroup. The results showed that the JXLC1806-2 virus, which was named as Lichuan virus, is a new member of Tete serogroup, and this is the first time that a Tete serogroup virus has been isolated in China

Human and animal exposure to newly discovered sand fly viruses, China

IntroductionThe Hedi virus (HEDV) and Wuxiang virus (WUXV) are newly discovered Bunyaviruses transmitted by sandflies. The geographical distribution of isolation of these two viruses continues to expand and it has been reported that WUXV causes neurological symptoms and even death in suckling mice. However, little is known about the prevalence of the two viruses in mammalian infections.MethodsIn order to understand the infection status of HEDV and WUXV in humans and animals from regions where the viruses have been isolated, this study used Western blotting to detect the positive rates of HEDV and WUXV IgG antibodies in serum samples from febrile patients, dogs, and chickens in the forementioned regions.ResultsThe results showed that of the 29 human serum samples, 17.24% (5/29) tested positive for HEDV, while 68.96% (20/29) were positive for WUXV. In the 31 dog serum samples, 87.10% (27/31) were positive for HEDV and 70.97% (22/31) were positive for WUXV, while in the 36 chicken serum samples, 47.22% (17/36) were positive for HEDV, and 52.78% (19/36) were positive for WUXV.DiscussionThese findings suggest there are widespread infections of HEDV and WUXV in mammals (dogs, chickens) and humans from the regions where these viruses have been isolated. Moreover, the positive rate of HEDV infections was higher in local animals compared to that measured in human specimens. This is the first seroepidemiological study of these two sandfly-transmitted viruses. The findings of the study have practical implications for vector-borne viral infections and related zoonotic infections in China, as well as providing an important reference for studies on the relationship between sandfly-transmitted viruses and zoonotic infections outside of China

Keyword-citation-keyword network: A new method for discipline knowledge structure analysis

As an important analysis method in bibliometrics, co-word analysis is used to map knowledge domain and discover the discipline knowledge structure based on the co-occurrence relationship between keywords in articles. In view of the problem existed in the traditional methods that the importance of keywords is not distinguished by the article and the co-occurrence of keywords is limited to the same article, the citation network is combined with the co-word analysis in this paper and a Keyword-Citation-Keyword (KCK) network is constructed. Then an empirical study is conducted in the computer science domain and the Mapping Knowledge Domain is generated by Gephi. The results indicate that compared with the traditional co-word network, the proposed method not only shows a better clustering performance but also discovers the important intellectual structure

A Preliminary Research on Term Function-aware Knowledge Unit Citation Network

Considering that the semantic roles of terms are not distinguished in the current knowledge unit citation network, we introduce term function to enhance it and propose a novel knowledge network called term function-aware knowledge unit citation network. A seq2seq model is utilized to identify question and method terms for scientific literature. Then, network structure analysis and knowledge community analysis are conducted on the constructed question-method terms citation network. A total of 156,805 articles with 109,951 question terms and 112,173 method terms, as well as 467,050 citation links collected from the Association for Computing Machinery digital library are used for empirical study. Results show that the proposed network possesses some unique structural characteristics and outperforms in knowledge community analysis. Findings from this study contribute to a new perspective for domain intellectual structure study

Keyword-citation-keyword network: A new method for discipline knowledge structure analysis

As an important analysis method in bibliometrics, co-word analysis is used to map knowledge domain and discover the discipline knowledge structure based on the co-occurrence relationship between keywords in articles. In view of the problem existed in the traditional methods that the importance of keywords is not distinguished by the article and the co-occurrence of keywords is limited to the same article, the citation network is combined with the co-word analysis in this paper and a Keyword-Citation-Keyword (KCK) network is constructed. Then an empirical study is conducted in the computer science domain and the Mapping Knowledge Domain is generated by Gephi. The results indicate that compared with the traditional co-word network, the proposed method not only shows a better clustering performance but also discovers the important intellectual structure