scholarly journals THE ROLE OF THE QUESTION-ANSWER FORM OF WRITING IN THE «WRITER'S DIARY»

2021 ◽  
Author(s):  
Valeriya K. Marchenko ◽  
Keyword(s):  
Text Processing ◽  
Automatic Text

This article is devoted to the study of the role of the question-answer form of writing in the "Writer’s Diary" by F.M. Dostoevsky using automatic text processing programs.

scholarly journals Lexical and grammatical features of Internet reviews in the Russian and English languages

2021 ◽  
Vol 3 (26) ◽  
pp. 107-115
Author(s):  
Elena I. Bojchuk ◽  
Keyword(s):  
Text Processing ◽  
Russian Language ◽  
Frequent Use ◽  
Distinguishing Features ◽  
Automatic Text ◽  
Tone Detection

The article presents an analysis of English and Russian-language Internet reviews of hotels in different countries and cities. The main purpose of the study is to identify the specific features of reviews in two languages in order to formulate clear criteria for automatic determining the tone of the text. On the basis of the existing works of linguists in this field, the author has identified the main directions of research on various issues related to the specifics of Internet reviews. Among others, gender and language specificity of reviews were noted, regardless of comparing their characteristics in two unrelated languages. The research undertaken focuses on the basic means of expressing positive, negative, or neutral tones in two languages, which is an interdisciplinary problem that combines work on the issues of selecting tone criteria for automatic text processing, as well as linguistic problems of language specificity of reviews, allowing to identify the main parameters for automatic tone detection on the basis of the most frequent specific features. The main results of the study are specific distinguishing features of English- and Russian-language reviews, the general and the particular in their characteristics. The most active means involved in the formation of a review are the frequent use of genre-specific lexemes, especially with positive or negative emotional and expressive coloring, combining words in a sentence, the role of negation, grammatical forms of verbs, the use of quantitative adverbs, adjectives in the superlative degree, etc.

Verification of Uncurated Protein Annotations

2010 ◽  
pp. 301-314
Author(s):  
Francisco M. Couto ◽  
Mário J. Silva ◽  
Vivian Lee ◽  
Emily Dimmer ◽  
Evelyn Camon ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Molecular Biology ◽  
Domain Knowledge ◽  
Text Processing ◽  
The Other ◽  
Research Projects ◽  
Evidence Text ◽  
Biological Sources ◽  
Biology Research ◽  
Automatic Text

Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires careful verification by curators, a painful and costly task, before being reliable enough to derive valid conclusions from it. On the other hand, research in biomedical information retrieval and information extraction are nowadays delivering Text Mining solutions that can support curators to improve the efficiency of their work to deliver better data resources. Over the past decades, automatic text processing systems have successfully exploited biomedical scientific literature to reduce the researchers’ efforts to keep up to date, but many of these systems still rely on domain knowledge that is integrated manually leading to unnecessary overheads and restrictions in its use. A more efficient approach would acquire the domain knowledge automatically from publicly available biological sources, such as BioOntologies, rather than using manually inserted domain knowledge. An example of this approach is GOAnnotator, a tool that assists the verification of uncurated protein annotations. It provided correct evidence text at 93% precision to the curators and thus achieved promising results. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.

scholarly journals Automated News Classification using N-gram Model and Key Features of Nepali Language

SCITECH Nepal ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 64-69
Author(s):  
Dinesh Dangol ◽  
Rupesh Dahi Shrestha ◽  
Arun Timalsina
Keyword(s):  
Text Classification ◽  
English Language ◽  
Promising Result ◽  
Text Processing ◽  
Automatic Text Classification ◽  
Key Features ◽  
Research Experiment ◽  
N Gram ◽  
Automated Text Processing ◽  
Automatic Text

With an increasing trend of publishing news online on website, automatic text processing becomes more and more important. Automatic text classification has been a focus of many researchers in different languages for decades. There is a huge amount of research repository on features of English language and their uses on automated text processing. This research implements Nepali language key features for automatic text classification of Nepali news. In particular, the study on impact of Nepali language based features, which are extremely different than English language is more challenging because of the higher level of complexity to be resolved. The research experiment using vector space model, n-gram model and key feature based processing specific to Nepali language shows promising result compared to bag-of-words model for the task of automated Nepali news classification.

scholarly journals Exploring Young Children’s Use of Illustrations in a Picturebook

2015 ◽  
Vol 17 (1) ◽  
pp. 42 ◽  
Author(s):  
Karen M. Feathers ◽  
Poonam Arya
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Oral Reading ◽  
Visual Information ◽  
Text Processing ◽  
Third Grade

Using analysis of oral reading and eye movements, this study examined how third grade children used visual information as they orally read either the original or the adapted version of a picturebook.  Eye tracking was examined to identify when and why students focused on images as well as what they looked at in the images.  Results document children’s deliberate use of images and point to the important role of images in text processing. The content of images, availability and placement of text and images on a page, and children’s personal strategies affected the use of images.  

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