scholarly journals Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Drug Safety ◽  
2014 ◽  
Vol 37 (10) ◽  
pp. 777-790 ◽  
Author(s):  
Rave Harpaz ◽  
Alison Callahan ◽  
Suzanne Tamang ◽  
Yen Low ◽  
David Odgers ◽  
...  
Keyword(s):  
Text Mining ◽  
Adverse Drug Events ◽  
State Of The Art

scholarly journals Graph Algorithms for Word Sense Disambiguation in Biomedicine

2015 ◽  
Author(s):  
Rodrigo Goulart ◽  
Juliano De Carvalho ◽  
Vera De Lima
Keyword(s):  
Text Mining ◽  
Graph Algorithms ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
The State ◽  
Word Sense ◽  
New Approach ◽  
Similar Performance ◽  
Sense Disambiguation ◽  
Different Levels

Word Sense Disambiguation (WSD) is an important task for Biomedicine text-mining. Supervised WSD methods have the best results but they are complex and their cost for testing is too high. This work presents an experiment on WSD using graph-based approaches (unsupervised methods). Three algorithms were tested and compared to the state of the art. Results indicate that similar performance could be reached with different levels of complexity, what may point to a new approach to this problem.

scholarly journals Text Mining in Cybersecurity: Exploring Threats and Opportunities

2019 ◽  
Vol 3 (3) ◽  
pp. 62 ◽  
Author(s):  
Maaike H. T. de Boer ◽  
Babette J. Bakker ◽  
Erik Boertjes ◽  
Mike Wilmer ◽  
Stephan Raaijmakers ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
State Of The Art ◽  
Data Sources ◽  
Added Value ◽  
Design Decision ◽  
User Evaluation ◽  
User Query ◽  
Overall Evaluation ◽  
Speed Up

The number of cyberattacks on organizations is growing. To increase cyber resilience, organizations need to obtain foresight to anticipate cybersecurity vulnerabilities, developments, and potential threats. This paper describes a tool that combines state of the art text mining and information retrieval techniques to explore the opportunities of using these techniques in the cybersecurity domain. Our tool, the Horizon Scanner, can scrape and store data from websites, blogs and PDF articles, and search a database based on a user query, show textual entities in a graph, and provide and visualize potential trends. The aim of the Horizon Scanner is to help experts explore relevant data sources for potential threats and trends and to speed up the process of foresight. In a requirements session and user evaluation of the tool with cyber experts from the Dutch Defense Cyber Command, we explored whether the Horizon Scanner tool has the potential to fulfill its aim in the cybersecurity domain. Although the overall evaluation of the tool was not as good as expected, some aspects of the tool were found to have added value, providing us with valuable insights into how to design decision support for forecasting analysts.

Text Mining in Bioinformatics

2013 ◽  
Vol 3 (2) ◽  
pp. 30-39 ◽  
Author(s):  
Yanliang Qi
Keyword(s):  
Text Mining ◽  
State Of The Art ◽  
Rapid Development ◽  
Exponential Rate ◽  
Data Set ◽  
Research Directions ◽  
Main Research ◽  
Needed Information ◽  
Huge Data ◽  
Daunting Task

Biomedical literatures have been increased at the exponential rate. To find the useful and needed information from such a huge data set is a daunting task for users. Text mining is a powerful tool to solve this problem. In this paper, we surveyed on text mining in Bioinformatics with emphasis on applications of text mining for bioinformatics. In this paper, the main research directions of text mining in bioinformatics are accompanied with detailed examples. This paper suited the need for the state-of-the-art of the field of text mining in Bioinformatics because of the rapid development in both text mining and bioinformatics. Finally, the problems and future way are identified at last.

Evaluation of terms and term extraction systems

Terminology ◽  
2007 ◽  
Vol 13 (2) ◽  
pp. 225-248 ◽  
Author(s):  
Jorge Vivaldi ◽  
Horacio Rodríguez
Keyword(s):  
Text Mining ◽  
Natural Language ◽  
Communication Systems ◽  
Ad Hoc ◽  
State Of The Art ◽  
Mining Activity ◽  
Evaluation Framework ◽  
Evaluation Methodology ◽  
Maturity Stage ◽  
Term Extraction

Term extraction may be defined as a text mining activity whose main purpose is to obtain all the terms included in a text of a given domain. Since the eighties, and mainly due to the rapid scientific advances as well as the evolution of the communication systems, there has been a growing interest in obtaining the terms found in written documents. A number of techniques and strategies have been proposed for satisfying this requirement. At present it seems that term extraction has reached a maturity stage. Nevertheless, many of the systems proposed fail to qualitatively present their results, almost every system evaluates its abilities in an ad hoc manner (if any, many times). Often, the authors do not explain their evaluation methodology; therefore comparisons between different implementations are difficult to draw. In this paper, we review the state-of-the-art of term extraction systems evaluation in the framework of natural language systems evaluation. The main approaches are presented, with a focus on their limitations. As an instantiation of some ideas for overcoming these limitations, the evaluation framework is applied to YATE, a hybrid term extractor.

scholarly journals A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time

2019 ◽  
Vol 1 (1) ◽  
pp. 2765-2774 ◽  
Author(s):  
Filippo Chiarello ◽  
Nicola Melluso ◽  
Andrea Bonaccorsi ◽  
Gualtiero Fantoni
Keyword(s):  
Text Mining ◽  
Engineering Design ◽  
State Of The Art ◽  
Topic Modelling ◽  
Specific Topic ◽  
Scientific Publications ◽  
New Knowledge ◽  
New Research ◽  
Large Corpus ◽  
Over Time

AbstractThe Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

Sentiment Analysis of Code-Mixed Text: A Review

2021 ◽  
Vol 12 (3) ◽  
pp. 2469-2478
Author(s):  
Nurul Husna Mahadzir Et.al
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
Qualitative Comparison ◽  
Analysis Process ◽  
Active Research ◽  
Multiple Languages

In recent times, sentiment analysis has become one of the most active research and progressively popular areas in information retrieval and text mining. To date, sentiment analysis has been applied in various domains such as product, movie, sport and political reviews. Most of the previous work in this field has focused on analyzing only a single language, especially English. However, with the need of globalization and the increasing number of the Internet used worldwide; it is common to see the post written in multiple languages. Moreover, in an unstructured content like Twitter posts, people tend to mix languages in one sentence, which make sentiment analysis process even harder and more challenging. This paper reviews the state-of-the-art of sentiment analysis for code-mixed, which includes the detail discussions of each focus area, qualitative comparison and limitations of current approaches. This paper also highlights challenges along this line of research and suggests several recommendations for future works that should be explored.

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