scholarly journals $NLP: How to spend a billion dollars

2022 ◽  
Vol 28 (1) ◽  
pp. 125-136
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Early Stage ◽  
Start Ups

AbstractFunding for AI start-ups in general is booming, and natural language processing as a subfield has not missed out. We take a closer look at early-stage funding over the last year—just over US$1B in total—for companies that offer solutions that are based on or make significant use of NLP, providing a picture of what funders think is innovative and bankable in this space, and we make some observations on notable trends and developments.

scholarly journals Detecting Early Stage Dementia based on Natural Language Processing

2019 ◽  
Vol 34 (4) ◽  
pp. B-J11_1-9
Author(s):  
Daisaku Shibata ◽  
Kaoru Ito ◽  
Shoko Wakamiya ◽  
Eiji Aramaki
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Early Stage

scholarly journals Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

2020 ◽  
Vol 12 (19) ◽  
pp. 7848 ◽  
Author(s):  
Israel Griol-Barres ◽  
Sergio Milla ◽  
Antonio Cebrián ◽  
Huaan Fan ◽  
Jose Millet
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Weak Signals ◽  
Remote Sensors ◽  
The Future ◽  
Increasing Demand ◽  
New Applications ◽  
Start Ups

Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.

scholarly journals Analysis of Therapy Transcripts using Natural Language Processing

2020 ◽  
Vol 9 (6) ◽  
pp. 484-493
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Early Stage ◽  
Support Vector ◽  
Analysis Tool ◽  
After Effects ◽  
Positive Outlook ◽  
Therapy Sessions

Mental health plays an integral part in leading a healthy life and having a positive outlook. This impacts our behavior, thought process, and actions and therefore it’simportant to identify and detect mental disorders in an early stage as it’s effects can have a lasting influence on one’s life. According to WHO, one in four people get affected by mental health disorders and currently 450 million people suffer from such conditions. Natural Language Processing can be a useful tool to analyze the trends in therapy transcripts. They can be further trained and optimized to derive useful insights and predict plausible future trends. Our proposed system analyses therapy transcripts and classifies it as ’Early signs of depression’ and ’Serious after-effects of prolonged depression’ based on the nature of the responses. Our system uses three different classifiers- Naïve Bayes, Support Vector Machine, and Logistic regression as well as two different victories- TF-IDF and Count, to classify the text into these categories. This proposed system will not only help patients in identifying their symptoms but will also help therapists and researchers in gathering a large amount of data which could be used in predictive analysis, diagnosis and understanding the patient. Such research will pave the way for improving counselling and therapy sessions and be a very essential analysis tool for therapists

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.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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