paperswithcode a website that collects research papers in computer science with together with their code artifacts, this link is to so a section on natural language texts summarization. CX_DB8 a modern queryable summarizer utilizing the latest in pre-trained language models.Word...
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Diacritical marks (diacritics for short) are various ascender and descender marks that, in combination with a letter, change its sound, tone, stress, and sometimes even meaning. The five most popular diacritics are the acute accent ("áéíóú"), the grave accent ("àèìòù"), circumflex (...
This endeavor is underscored by the significant prevalence of diabetes as a chronic ailment in China, with its potential to engender a multitude of grave complications [13]. In the field of diabetes, Diabetes Mellitus Treatment Ontology (DMTO) is a high-quality English ontology [14], which ...
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare
With these links, the user can obtain a detailed overview of the specific gene or protein of interest. Figure 1 Disease query for the MeSH term Grave's disease. Screen shot for the MeSH term "Graves' disease". The first column (Gene) contains the gene name. Moving with the mouse over ...
Named entity recognition (NER) is a task of detecting named entities in documents and categorizing them to predefined classes, such as person, location, and organization. This paper focuses on tweets posted on Twitter. Since tweets are noisy, irregular,
I took the best one and added the link to this picture. Then I used “vary: strong” on the best ones (a new feature I love, the previous variance wasn’t enough). I finally got something (right) which is pretty similar, but also has less interesting personality and feels a little ...
baseurl, link['href'])) if has_key(link, 'title'): self.out(" ("+link['title']+")") self.out("\n") else: newa.append(link)if self.a != newa: self.out("\n") # Don't need an extra line when nothing was done.
Likewise, text features at the frame-level were extracted from words and short phrases (i.e., below the sentence level) of the transcribed speech (relying on a speech-to-text service), rather than employing the complete transcription of each subject’s interview, where the latter was utilized...