scholarly journals Extraction of phenotypic traits from taxonomic descriptions for the tree of life using natural language processing

2018 ◽  
Vol 6 (3) ◽  
pp. e1035 ◽  
Author(s):  
Lorena Endara ◽  
Hong Cui ◽  
J. Gordon Burleigh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Tree Of Life ◽  
Phenotypic Traits ◽  
Taxonomic Descriptions

scholarly journals Automated Trait Extraction using ClearEarth, a Natural Language Processing System for Text Mining in Natural Sciences

2018 ◽  
Vol 2 ◽  
pp. e26080 ◽  
Author(s):  
Anne Thessen ◽  
Jenette Preciado ◽  
Payoj Jain ◽  
James Martin ◽  
Martha Palmer ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing System ◽  
Natural Sciences ◽  
Phenotypic Traits ◽  
Good Precision ◽  
Named Entities ◽  
Clinical Notes ◽  
Linguistic Annotation

The cTAKES package (using the ClearTK Natural Language Processing toolkit Bethard et al. 2014,http://cleartk.github.io/cleartk/) has been successfully used to automatically read clinical notes in the medical field (Albright et al. 2013, Styler et al. 2014). It is used on a daily basis to automatically process clinical notes and extract relevant information by dozens of medical institutions. ClearEarth is a collaborative project that brings together computational linguistics and domain scientists to port Natural Language Processing (NLP) modules trained on the same types of linguistic annotation to the fields of geology, cryology, and ecology. The goal for ClearEarth in the ecology domain is the extraction of ecologically-relevant terms, including eco-phenotypic traits from text and the assignment of those traits to taxa. Four annotators used Anafora (an annotation software; https://github.com/weitechen/anafora) to mark seven entity types (biotic, aggregate, abiotic, locality, quality, unit, value) and six reciprocal property types (synonym of/has synonym, part of/has part, subtype/supertype) in 133 documents from primarily Encyclopedia of Life (EOL) and Wikipedia according to project guidelines (https://github.com/ClearEarthProject/AnnotationGuidelines). Inter-annotator agreement ranged from 43% to 90%. Performance of ClearEarth on identifying named entities in biology text overall was good (precision: 85.56%; recall: 71.57%). The named entities with the best performance were organisms and their parts/products (biotic entities - precision: 72.09%; recall: 54.17%) and systems and environments (aggregate entities - precision: 79.23%; recall: 75.34%). Terms and their relationships extracted by ClearEarth can be embedded in the new ecocore ontology after vetting (http://www.obofoundry.org/ontology/ecocore.html). This project enables use of advanced industry and research software within natural sciences for downstream operations such as data discovery, assessment, and analysis. In addition, ClearEarth uses the NLP results to generate domain-specific ontologies and other semantic resources.

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.

PRINCIPAL PROBLEMS OF NATURAL LANGUAGE PROCESSING SYSTEMS

2018 ◽  
pp. 35-38
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Analysis ◽  
Syntactic Ambiguity ◽  
Grammatical Structure ◽  
English Sentence ◽  
Analysis Methods ◽  
The Way

The article deals with natural language processing, namely that of an English sentence. The article describes the problems, which might arise during the process and which are connected with graphic, semantic, and syntactic ambiguity. The article provides the description of how the problems had been solved before the automatic syntactic analysis was applied and the way, such analysis methods could be helpful in developing new analysis algorithms. The analysis focuses on the issues, blocking the basis for the natural language processing — parsing — the process of sentence analysis according to their structure, content and meaning, which aims to analyze the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them.

Sign in / Sign up

Export Citation Format

Share Document