scholarly journals Interactive Exploration of the Readability of Science Authors

2021 ◽  
Author(s):  
Russell J Jarvis ◽  
Patrick M. McGurrin ◽  
Rebecca Featherston ◽  
Marc Skov Madsen ◽  
Shivam Bansal ◽  
...  
Keyword(s):  
Natural Language ◽  
Text Analysis ◽  
Journal Article ◽  
Data Driven ◽  
Analysis Tool ◽  
Single Text ◽  
Search Service ◽  
Text Types ◽  
Data Driven Approach ◽  
Academic Authors

Here we present a new text analysis tool that consists of a text analysis service and an author search service. These services were created by using or extending many existing Free and Open Source tools, including streamlit, requests, WordCloud, TextStat, and The Natural Language Tool Kit. The tool has the capability to retrieve journal hosting links and journal article content from APIs and journal hosting websites. Together, these services allow the user to review the complexity of a scientist’s published work relative to other online-based text repositories. Rather than providing feedback as to the complexity of a single text as previous tools have done, the tool presented here shows the relative complexity across many texts from the same author, while also comparing the readability of the author’s body of work to a variety of other scientific and lay text types. The goal of this work is to apply a more data-driven approach that provides established academic authors with statistical insights into their body of published peer reviewed work. By monitoring these readability metrics, scientists may be able to cater their writing to reach broader audiences, contributing to an improved global communication and understanding of complex topics.

scholarly journals Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

2020 ◽  
pp. 221-251
Author(s):  
Stefan Varga ◽  
Joel Brynielsson ◽  
Andreas Horndahl ◽  
Magnus Rosell
Keyword(s):  
Text Analysis ◽  
Ecological Validity ◽  
Data Driven ◽  
Intelligence Analysis ◽  
Prototype Tool ◽  
Analysis Task ◽  
Private Citizens ◽  
Data Driven Approach ◽  
Psychological Operations

Abstract With the availability of an abundance of data through the Internet, the premises to solve some intelligence analysis tasks have changed for the better. The study presented herein sets out to examine whether and how a data-driven approach can contribute to solve intelligence tasks. During a full day observational study, an ordinary military intelligence unit was divided into two uniform teams. Each team was independently asked to solve the same realistic intelligence analysis task. Both teams were allowed to use their ordinary set of tools, but in addition one team was also given access to a novel text analysis prototype tool specifically designed to support data-driven intelligence analysis of social media data. The results, obtained from the case study with a high ecological validity, suggest that the prototype tool provided valuable insights by bringing forth information from a more diverse set of sources, specifically from private citizens that would not have been easily discovered otherwise. Also, regardless of its objective contribution, the capabilities and the usage of the tool were embraced and subjectively perceived as useful by all involved analysts.

Convergences and divergences between studies on translator training and interpreter training

Target ◽  
2017 ◽  
Vol 29 (1) ◽  
pp. 110-144 ◽  
Author(s):  
Jun Pan ◽  
Honghua Wang ◽  
Jackie Xiu Yan
Keyword(s):  
Journal Article ◽  
Translation Studies ◽  
Data Driven ◽  
Methodological Framework ◽  
Content Type ◽  
Research Themes ◽  
Translator Training ◽  
Data Driven Approach ◽  
Interpreter Training

Discussion on the convergences and divergences between Translation Studies (TS) and Interpreting Studies (IS) has taken place since the emergence of the latter. The unity and divide between translation and interpreting (T&I) activities are also strongly felt in the field of training. This study adopts a data-driven approach to examine research on translator and interpreter training. Based on an annotated database of T&I journal article entries, it shows the differences and similarities in research on translator training and interpreter training. Findings suggest that research on translator training and interpreter training share a thematic and methodological framework, but have different focuses on research themes and methods. The two sub-disciplines have different active authors, institutions and country/area rankings, linked by a small yet possibly growing number of cross-sub-disciplinary producers. These findings will shed much light on our knowledge of T&I activities and research.

scholarly journals A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations

2017 ◽  
Author(s):  
Kangqi Luo ◽  
Xusheng Luo ◽  
Xianyang Chen ◽  
Kenny Q. Zhu
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Base ◽  
Search Algorithm ◽  
Knowledge Bases ◽  
Data Driven ◽  
Learning Approach ◽  
Data Driven Approach ◽  
Structured Knowledge

This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.

scholarly journals Mapping the semantic organization of the English odor vocabulary using natural language data

2020 ◽  
Author(s):  
Thomas Hörberg ◽  
Maria Larsson ◽  
Jonas Olofsson
Keyword(s):  
Natural Language ◽  
Full Range ◽  
Semantic Space ◽  
Data Driven ◽  
Semantic Organization ◽  
Language Data ◽  
Data Driven Approach ◽  
Descriptive System ◽  
Small Set ◽  
Odor Descriptors

Olfactory experiences are hard to verbalize, partly because most languages lack devoted odor vocabularies. Yet, there is a need for a standardized odor vocabulary, but no descriptive system for describing the full range of odor experiences has been agreed upon. Many studies of the English odor vocabulary have been based on perceptual data such as odor-descriptor ratings, thereby being limited to a small set of pre-selected descriptors. In the present study, we present a data-driven approach that automatically identifies odor descriptors in English, and then derive their semantic organization on the basis of their distributions in natural texts. Olfactory descriptors are automatically identified on the basis of their degree of olfactory association, and their semantic organization is derived with a distributional-semantic word embedding model. We identify and derive the semantic organization of the descriptors most frequently used to describe odors and flavors in English, both within and across source-based, abstract and evaluative descriptor categories. Our method is to a large extent able to capture semantic differences between descriptors related to aroma and flavor qualities, rather than e.g. functional or linguistic aspects, in that it primarily differentiates descriptors with respect to valence and edibility, and the semantic space derived from it is qualitatively similar to a space derived from perceptual data.

DARR: A Free-text Analysis System for the Automatic Documentation of Radiological Reports

1977 ◽  
Vol 16 (03) ◽  
pp. 144-153 ◽  
Author(s):  
E. Vaccari ◽  
W. Delaney ◽  
A. Chiesa
Keyword(s):  
Natural Language ◽  
Text Analysis ◽  
Automatic Documentation ◽  
Free Text ◽  
Software System ◽  
Automatic Translation ◽  
Radiological Report ◽  
Analysis System ◽  
Content Processing

A software system for the automatic free-text analysis and retrieval of radiological reports is presented. Such software involves: (1) automatic translation of the specific natural language in a formalized metalanguage in order to transform the radiological report in a »normalized report« analyzable by computer; (2) content processing of the normalized report to select desired information. The approach used to accomplish point (1) is described in detail referring to a specific application.

Data Driven Approach to Forecast Building Occupant Complaints

2020 ◽  
Author(s):  
Sena Assaf ◽  
Mohamad Awada ◽  
Issam Srour
Keyword(s):  
Data Driven ◽  
Data Driven Approach

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional
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