The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275

Англійська мова для навчання і работи. Навчальний посібник з англійської мови за професійним спрямуванням для студентів і фахівців галузі знань 0503 Розробка корисних копалин Т 1

A coursebook includes all the activities of students’ work at ESP course aimed at development of language behaviour necessary for effective communication of students in their study and specialism areas. The tasks and activities given in the coursebook are typicalfor students’ academic and professional domains and situations. The content is organized in modules that covers generic job-related language skills of engineers. The authentic texts taken from real life contain interesting up-to-date information about mining, peculiarities of study abroad, customs and traditions of English-speaking countries. Pack of self-study resources given in Part II contains Glossary of mining terms, tasks and activities aimed at developing a range of vocabulary necessary for mining, different functions and functional exponents to be used in academic and professional environment as well as tasks developing self-awareness, self-assessment and self-organisation skills. Testing points for different grammar structuresare given in Part III. Indices at the end of each part easify the use of the coursebook. The coursebook contains illustrations, various samples of visualizing technical information. The coursebook is designed for ESP students of non-linguistic universities. It can be used as teaching/learning materials for ESP Courses for Mining Engineers as well as for self-study of subject and specialist teachers, practicing mining engineers and researchers in Engineering.У посібнику представлені всі види діяльності студентів з вивчення англійської мови, спрямовані на розвиток мовної поведінки, необхідної для ефективного спілкування в академічному та професійному середовищах. Навчальний посібник містить завдання і вправи, типові для різноманітних академічних та професійних сфер і ситуацій. Структура організації змісту– модульна і охоплює загальні мовленнєві вміння інженерів. Зразки текстів– автентичні, взяті з реального життя, містять цікаву та актуальну інформацію про видобувничу промисловість, особливості навчання за кордоном, традиції та звичаї країн, мова яких вивчається. Ресурси для самостійної роботи(Том ІІ) містять глосарій термінів, завдання та вправи для розвитку словарного запасу та розширення діапазону функціональних зразків, необхідних для виконання певних функцій, та завдання, які спрямовані на розвиток навичок самооцінювання і організації свого навчання. Граматичні явища і вправи для їх засвоєння наводяться в томі ІІІ. Наприкінці кожної частини наведено алфавітно-предметні покажчики. Багато ілюстрацій та різних візуальних засобів подання інформації. Навчальний посібник призначений для студентів технічних університетів гірничого профілю. Може використовуватися для самостійного вивчення англійської мови викладачами, фахівцями і науковцями різних інженерних галузей

ACQUA: Automated Community-based Question Answering through the Discretisation of Shallow Linguistic Features

This paper addresses the problem of determining the best answer in Community-based Question Answering (CQA) websites by focussing on the content. In particular, we present a novel system, ACQUA (http://acqua.kmi.open.ac.uk), that can be installed onto the majority of browsers as a plugin. The service offers a seamless and accurate prediction of the answer to be accepted. Our system is based on a novel approach for processing answers in CQAs. Previous research on this topic relies on the exploitation of community feedback on the answers, which involves rating of either users (e.g., reputation) or answers (e.g. scores manually assigned to answers). We propose a new technique that leverages the content/textual features of answers in a novel way. Our approach delivers better results than related linguistics-based solutions and manages to match rating-based approaches. More specifically, the gain in performance is achieved by rendering the values of these features into a discretised form. We also show how our technique manages to deliver equally good results in real-time settings, as opposed to having to rely on information not always readily available, such as user ratings and answer scores. We ran an evaluation on 21 StackExchange websites covering around 4 million questions and more than 8 million answers. We obtain 84% average precision and 70% recall, which shows that our technique is robust, effective, and widely applicable

Machine translation evaluation resources and methods: a survey

We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT. This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content