Preprocessing in Biomedical Literature Mining Using Natural Language Processing

2014 ◽  
Vol 687-691 ◽  
pp. 1149-1152
Author(s):  
Jing Peng ◽  
Hong Min Sun
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Biomedical Literature ◽  
Literature Mining ◽  
Noise Data ◽  
Text Preprocessing ◽  
Biomedical Literature Mining ◽  
The Web

The number of biomedical literatures is growing rapidly, and biomedical literature mining is becoming essential. An approach for article processing in text preprocessing is proposed in order to improve the performance of biomedical literature mining. This approach combines the Web and corpus counts in order to eliminate the limitations of noise data of the Web. We experimentally showed that the performance of the combination models is the best comparing to the pure Web and corpus models. We achieve the best precision of 89.1% on all article forms and 88.7% article loss class.

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

scholarly journals Natural Language to SQL query Generation

2021 ◽  
Vol 9 (VI) ◽  
pp. 5069-5072
Author(s):  
Kiran Raj R
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Regular Expression ◽  
Parts Of Speech ◽  
Query Generation ◽  
Sql Query ◽  
Speech Tagging ◽  
The Web

Today, everyone has a personal device to access the web. Every user tries to access the knowledge that they require through internet. Most of the knowledge is within the sort of a database. A user with limited knowledge of database will have difficulty in accessing the data in the database. Hence, there’s a requirement for a system that permits the users to access the knowledge within the database. The proposed method is to develop a system where the input be a natural language and receive an SQL query which is used to access the database and retrieve the information with ease. Tokenization, parts-of-speech tagging, lemmatization, parsing and mapping are the steps involved in the process. The project proposed would give a view of using of Natural Language Processing (NLP) and mapping the query in accordance with regular expression in English language to SQL.

scholarly journals Using distant supervision to augment manually annotated data for relation extraction

2019 ◽  
Author(s):  
Peng Su ◽  
Gang Li ◽  
Cathy Wu ◽  
K. Vijay-Shanker
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Training Data ◽  
Distant Supervision ◽  
Large Size ◽  
Domain Expertise

AbstractSignificant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.

scholarly journals Using of Natural Language Processing Techniques in Suicide Research

2017 ◽  
Vol 1 (2) ◽  
pp. 89 ◽  
Author(s):  
Azam Orooji ◽  
Mostafa Langarizadeh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Information ◽  
Inclusion Criteria ◽  
Data Set ◽  
Completed Suicide ◽  
Teenagers And Young Adults ◽  
Processing Techniques ◽  
The Web

It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

Related Blogs’ Summarization With Natural Language Processing

2020 ◽  
Author(s):  
Niyati Baliyan ◽  
Aarti Sharma
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The Web ◽  
Content Generation

Abstract There is plethora of information present on the web, on a given topic, in different forms i.e. blogs, articles, websites, etc. However, not all of the information is useful. Perusing and going through all of the information to get the understanding of the topic is a very tiresome and time-consuming task. Most of the time we end up investing in reading content that we later understand was not of importance to us. Due to the lack of capacity of the human to grasp vast quantities of information, relevant and crisp summaries are always desirable. Therefore, in this paper, we focus on generating a new blog entry containing the summary of multiple blogs on the same topic. Different approaches of clustering, modelling, content generation and summarization are applied to reach the intended goal. This system also eliminates the repetitive content giving savings on time and quantity, thereby making learning more comfortable and effective. Overall, a significant reduction in the number of words in the new blog generated by the system is observed by using the proposed novel methodology.

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