SPOKEN QA BASED ON A PASSAGE RETRIEVAL ENGINE

Author(s):  
Emilio Sanchis ◽  
Davide Buscaldi ◽  
Sergio Grau ◽  
Lluis Hurtado ◽  
David Griol
2009 ◽  
Vol 34 (2) ◽  
pp. 113-134 ◽  
Author(s):  
Davide Buscaldi ◽  
Paolo Rosso ◽  
José Manuel Gómez-Soriano ◽  
Emilio Sanchis

2017 ◽  
Vol 9 (1) ◽  
pp. 19-24 ◽  
Author(s):  
David Domarco ◽  
Ni Made Satvika Iswari

Technology development has affected many areas of life, especially the entertainment field. One of the fastest growing entertainment industry is anime. Anime has evolved as a trend and a hobby, especially for the population in the regions of Asia. The number of anime fans grow every year and trying to dig up as much information about their favorite anime. Therefore, a chatbot application was developed in this study as anime information retrieval media using regular expression pattern matching method. This application is intended to facilitate the anime fans in searching for information about the anime they like. By using this application, user can gain a convenience and interactive anime data retrieval that can’t be found when searching for information via search engines. Chatbot application has successfully met the standards of information retrieval engine with a very good results, the value of 72% precision and 100% recall showing the harmonic mean of 83.7%. As the application of hedonic, chatbot already influencing Behavioral Intention to Use by 83% and Immersion by 82%. Index Terms—anime, chatbot, information retrieval, Natural Language Processing (NLP), Regular Expression Pattern Matching


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Swapna Vidhur Daulatabad ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga

Abstract Background With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare. Results We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed’s abstract retrieval engine and NCBO’s recommender annotation system. Lantern’s annotations were benchmarked against lncRNAdb’s manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform. Conclusions Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.


1995 ◽  
Vol 27 (6) ◽  
pp. 1017-1026 ◽  
Author(s):  
Alexander Clausnitzer ◽  
Pavel Vogel ◽  
Stephan Wiesener
Keyword(s):  

Author(s):  
Hassan Saneifar ◽  
Stéphane Bonniol ◽  
Anne Laurent ◽  
Pascal Poncelet ◽  
Mathieu Roche
Keyword(s):  

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