scholarly journals An Examination of Expert Discourse on Human Gene Editing Using Natural Language Processing

2020 ◽  
Author(s):  
Micah Musser
Keyword(s):  
Natural Language Processing ◽  
Key Words ◽  
Language Processing ◽  
Gene Editing ◽  
Human Gene ◽  
Global Politics ◽  
The Public ◽  
New Gene ◽  
Research Questions ◽  
Key Terms

This research aims to explore the different ways in which scientists, ethicists, journalists, and officialcommissions speak to the public about new gene editing technologies. After building and preprocessingdatasets of writings from each type of author, I examined frequencies of a number of key words, traineda sentiment classifier based on 5,000 hand-labeled sentences, and explored the type of sentiments used todiscuss different issues. Key research questions included the degree of optimism or pessimism expressedby each type of author, the sentiment used in discussions about the global politics of gene editing, and thefrequency with which key terms are used by each category of expert.

News Headline Building using Hybrid Headline Generation Technique for Quick Gist

2017 ◽  
Vol 6 (1) ◽  
pp. 36-52
Author(s):  
Urmila Shrawankar ◽  
Kranti Wankhede
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Reading Time ◽  
News Article ◽  
Keyword Extraction ◽  
Keyphrase Extraction ◽  
Generation Technique ◽  
Extraction Algorithm ◽  
Key Terms

A considerable amount of time is required to interpret whole news article to get the gist of it. Therefore, in order to reduce the reading and interpretation time, headlines are necessary. The available techniques for news headline construction mainly includes extractive and abstractive headline generation techniques. In this paper, context based news headline is formed from long news article by using techniques of core Natural Language Processing (NLP) and key terms of news article. Key terms are retrieved from lengthy news article by using various approaches of keyword extraction. The keyphrases are picked out using Keyphrase Extraction Algorithm (KEA) which helps to construct headline syntax along with NLP's parsing technique. Sentence compression algorithm helps to generate compressed sentences from generated parse tree of leading sentences. Headline helps user for reducing cognitive burden of reader by reflecting important contents of news. The objective is to frame headline using key terms for reducing reading time and efforts of reader.

Engaging the Public at a Science Festival

2017 ◽  
Vol 39 (2) ◽  
pp. 250-277 ◽  
Author(s):  
Kathleen M. Rose ◽  
Kaine Korzekwa ◽  
Dominique Brossard ◽  
Dietram A. Scheufele ◽  
Laura Heisler
Keyword(s):  
Public Engagement ◽  
Gene Editing ◽  
Human Gene ◽  
The Public ◽  
Scientific Issue ◽  
Public Engagement With Science ◽  
Risk And Benefit ◽  
Science Activities

Theoretically and methodologically sound research on the reach and impact of public engagement practices continues to lag behind. Using the 2015 Wisconsin Science Festival as context, we empirically investigate the impacts of a public engagement activity about a nascent and controversial scientific issue, human gene editing. Overall, we find the panel increased participants’ understanding of the complexities of human gene editing, as demonstrated by increases in knowledge and the moral acceptability of the technology among respondents, as well as the associated risk and benefit perceptions. Practical and theoretical implications for science festivals and public engagement with science activities are discussed.

scholarly journals Finite-state methods and models in natural language processing

2011 ◽  
Vol 17 (2) ◽  
pp. 141-144
Author(s):  
ANSSI YLI-JYRÄ ◽  
ANDRÁS KORNAI ◽  
JACQUES SAKAROVITCH
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Current Issue ◽  
Special Issue ◽  
The Public ◽  
The Past ◽  
Finite State ◽  
Final Selection

For the past two decades, specialised events on finite-state methods have been successful in presenting interesting studies on natural language processing to the public through journals and collections. The FSMNLP workshops have become well-known among researchers and are now the main forum of the Association for Computational Linguistics' (ACL) Special Interest Group on Finite-State Methods (SIGFSM). The current issue on finite-state methods and models in natural language processing was planned in 2008 in this context as a response to a call for special issue proposals. In 2010, the issue received a total of sixteen submissions, some of which were extended and updated versions of workshop papers, and others which were completely new. The final selection, consisting of only seven papers that could fit into one issue, is not fully representative, but complements the prior special issues in a nice way. The selected papers showcase a few areas where finite-state methods have less than obvious and sometimes even groundbreaking relevance to natural language processing (NLP) applications.

Application of Natural Language Processing (NLP) Techniques in E–Governance

2009 ◽  
pp. 122-132 ◽  
Author(s):  
Siddhartha Ghosh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information And Communication Technologies ◽  
Citizen Participation ◽  
Language Processing ◽  
Good Governance ◽  
Communication Technologies ◽  
The Public ◽  
Information And Communication ◽  
The Masses

E-governance is the public sector’s use of information and communication technologies (ICT) with the aim of improving information and service delivery, encouraging citizen participation in the decision-making process and making government more accountable, transparent, and effective. Effective and efficient e-governments deploy information and communication technology systems to deliver services through multiple channels that are accessible, fast, secure, reliable, seamless, and coherent. To implement better government-to-government (G2G), government-to-business (G2B), government-to-enterprise (G2E) and government-to-citizen (G2C) services a good governance should not only utilize ICT, it has to be also serious about implementing natural language processing (NLP) Techniques to reach up to the masses and make e-governance successful one. This chapter shows the need of applying NLP technologies in the field of e-governance and also tries to focus on the issues, which can be resolved very easily with the help of these modern technologies. It also shows the advantages of applying NLP in e-governance.

A text-based multi-span network for reading comprehension

2021 ◽  
pp. 1-13
Author(s):  
Deguang Chen ◽  
Ziping Ma ◽  
Lin Wei ◽  
Yanbin Zhu ◽  
Jinlin Ma ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Market Value ◽  
The State ◽  
Experimental Results ◽  
Training Time ◽  
The Public

Text-based reading comprehension models have great research significance and market value and are one of the main directions of natural language processing. Reading comprehension models of single-span answers have recently attracted more attention and achieved significant results. In contrast, multi-span answer models for reading comprehension have been less investigated and their performances need improvement. To address this issue, in this paper, we propose a text-based multi-span network for reading comprehension, ALBERT_SBoundary, and build a multi-span answer corpus, MultiSpan_NMU. We also conduct extensive experiments on the public multi-span corpus, MultiSpan_DROP, and our multi-span answer corpus, MultiSpan_NMU, and compare the proposed method with the state-of-the-art. The experimental results show that our proposed method achieves F1 scores of 84.10 and 92.88 on MultiSpan_DROP and MultiSpan_NMU datasets, respectively, while it also has fewer parameters and a shorter training time.

scholarly journals Authorship Weightage Algorithm for Academic Publications: A New Calculation and ACES Webserver for Determining Expertise

2021 ◽  
Vol 4 (2) ◽  
pp. 41
Author(s):  
Wei-Ling Wu ◽  
Owen Tan ◽  
Kwok-Fong Chan ◽  
Nicole Bernadette Ong ◽  
David Gunasegaran ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Group ◽  
Scoring System ◽  
Scoring Systems ◽  
The Public ◽  
Academic Fields ◽  
Academic Publications ◽  
The Many

Despite the public availability, finding experts in any field when relying on academic publications can be challenging, especially with the use of jargons. Even after overcoming these issues, the discernment of expertise by authorship positions is often also absent in the many publication-based search platforms. Given that it is common in many academic fields for the research group lead or lab head to take the position of the last author, some of the existing authorship scoring systems that assign a decreasing weightage from the first author would not reflect the last author correctly. To address these problems, we incorporated natural language processing (Common Crawl using fastText) to retrieve related keywords when using jargons as well as a modified authorship positional scoring that allows the assignment of greater weightage to the last author. The resulting output is a ranked scoring system of researchers upon every search that we implemented as a webserver for internal use called the APD lab Capability & Expertise Search (ACES).

scholarly journals Opinion Mining Analysis of Twitter Users of Particular Topic

2020 ◽  
pp. 102-108
Author(s):  
Sneha Naik ◽  
Mona Mulchandani
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
The Public ◽  
Public Sentiment ◽  
Twitter Users

Opinion mining consists of many different fields like natural language processing, text mining, decision making and linguistics. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.

scholarly journals Fake News Detection with Machine Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 124-127
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online News ◽  
The Internet ◽  
Daily Routine ◽  
Fake News ◽  
Criminal Records ◽  
The Public ◽  
Processing Techniques

As the internet is becoming part of our daily routine there is sudden growth and popularity of online news reading. This news can become a major issue to the public and government bodies (especially politically) if its fake hence authentication is necessary. It is essential to flag the fake news before it goes viral and misleads the society. In this paper, various Natural Language Processing techniques along with the number of classifiers are used to identify news content for its credibility.Further this technique can be used for various applications like plagiarismcheck , checking for criminal records.

scholarly journals The Public AI Research Portfolio of China’s Security Forces: A High-Level Analysis

2021 ◽  
Author(s):  
Dewey Murdick ◽  
Daniel Chou ◽  
Ryan Fedasiuk ◽  
Emily Weinstein
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Language Processing ◽  
Chinese Language ◽  
Public Security ◽  
Security Forces ◽  
The Public ◽  
High Level ◽  
Level Analysis

New analytic tools are used in this data brief to explore the public artificial intelligence (AI) research portfolio of China’s security forces. The methods contextualize Chinese-language scholarly papers that claim a direct working affiliation with components of the Ministry of Public Security, People's Armed Police Force, and People’s Liberation Army. The authors review potential uses of computer vision, robotics, natural language processing and general AI research.

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