3D Visualization of Simple Natural Language Statement Using Semantic Description

2011 ◽  
pp. 36-44
Author(s):  
Rabiah Abdul Kadir ◽  
Abdul Rahman Mad Hashim ◽  
Rahmita Wirza ◽  
Aida Mustapha
Keyword(s):  
Natural Language ◽  
3D Visualization ◽  
Semantic Description ◽  
Natural Language Statement

scholarly journals nQuery - A Natural Language Statement to SQL Query Generator

2017 ◽  
Author(s):  
Nandan Sukthankar ◽  
Sanket Maharnawar ◽  
Pranay Deshmukh ◽  
Yashodhara Haribhakta ◽  
Vibhavari Kamble
Keyword(s):  
Natural Language ◽  
Sql Query ◽  
Natural Language Statement

scholarly journals Automated SQL Query Generator by Understanding a Natural Language Statement

2018 ◽  
Vol 7 (3) ◽  
pp. 01-11 ◽  
Author(s):  
Amit Pagrut ◽  
Ishant Pakmode ◽  
Shambhoo Kariya ◽  
Vibhavari Kamble ◽  
Yashodhara Haribhakta
Keyword(s):  
Natural Language ◽  
Sql Query ◽  
Natural Language Statement

Cognitive Models Underlying Students' Formulation of Simple Linear Equations

1993 ◽  
Vol 24 (3) ◽  
pp. 217-232 ◽  
Author(s):  
Mollie MacGregor ◽  
Kaye Stacey
Keyword(s):  
Natural Language ◽  
Linear Equations ◽  
Cognitive Model ◽  
Multiple Choice ◽  
Response Format ◽  
Algebraic Equations ◽  
Free Response ◽  
Multiple Choice Item ◽  
Reversal Error ◽  
Natural Language Statement

Data are presented to show that errors in formulating algebraic equations are not primarily due to syntactic translation, as has been assumed in the literature. Furthermore, it is shown that the reversal error is common even when none of the previously published causes of the error is applicable. A new explanation is required and is proposed in this paper. An examination of students' errors leads us to suggest that students generally construct from the natural language statement a cognitive model of compared unequal quantities. They formulate equations by trying to represent the model directly or by drawing information from it. This hypothesis is supported by research on the comprehension of relationships by linguists, pyscholinguists and psychologists. Data were collected from 281 students in grade 9 in free response format and from 1048 students in grades 8, 9, and 10 who completed a multiple-choice item.

scholarly journals PhageAI - Bacteriophage Life Cycle Recognition with Machine Learning and Natural Language Processing

2020 ◽  
Author(s):  
Piotr Tynecki ◽  
Arkadiusz Guziński ◽  
Joanna Kazimierczak ◽  
Michał Jadczuk ◽  
Jarosław Dastych ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Life Cycle ◽  
Natural Language ◽  
Language Processing ◽  
3D Visualization ◽  
Life Cycles ◽  
Nucleotide Sequences ◽  
Support Vector ◽  
Model Classification

AbstractBackgroundAs antibiotic resistance is becoming a major problem nowadays in a treatment of infections, bacteriophages (also known as phages) seem to be an alternative. However, to be used in a therapy, their life cycle should be strictly lytic. With the growing popularity of Next Generation Sequencing (NGS) technology, it is possible to gain such information from the genome sequence. A number of tools are available which help to define phage life cycle. However, there is still no unanimous way to deal with this problem, especially in the absence of well-defined open reading frames. To overcome this limitation, a new tool is definitely needed.ResultsWe developed a novel tool, called PhageAI, that allows to access more than 10 000 publicly available bacteriophages and differentiate between their major types of life cycles: lytic and lysogenic. The tool included life cycle classifier which achieved 98.90% accuracy on a validation set and 97.18% average accuracy on a test set. We adopted nucleotide sequences embedding based on the Word2Vec with Ship-gram model and linear Support Vector Machine with 10-fold cross-validation for supervised classification. PhageAI is free of charge and it is available at https://phage.ai/. PhageAI is a REST web service and available as Python package.ConclusionsMachine learning and Natural Language Processing allows to extract information from bacteriophages nucleotide sequences for lifecycle prediction tasks. The PhageAI tool classifies phages into either virulent or temperate with a higher accuracy than any existing methods and shares interactive 3D visualization to help interpreting model classification results.

BioNorm: deep learning-based event normalization for the curation of reaction databases

2019 ◽  
Author(s):  
Peiliang Lou ◽  
Antonio Jimeno Yepes ◽  
Zai Zhang ◽  
Qinghua Zheng ◽  
Xiangrong Zhang ◽  
...  
Keyword(s):  
Text Mining ◽  
Natural Language ◽  
Scientific Literature ◽  
Short Term Memory ◽  
Identification Problem ◽  
Molecular Reaction ◽  
Complete Networks ◽  
Novel Method ◽  
Types Of Information ◽  
Natural Language Statement

Abstract Motivation A biochemical reaction, bio-event, depicts the relationships between participating entities. Current text mining research has been focusing on identifying bio-events from scientific literature. However, rare efforts have been dedicated to normalize bio-events extracted from scientific literature with the entries in the curated reaction databases, which could disambiguate the events and further support interconnecting events into biologically meaningful and complete networks. Results In this paper, we propose BioNorm, a novel method of normalizing bio-events extracted from scientific literature to entries in the bio-molecular reaction database, e.g. IntAct. BioNorm considers event normalization as a paraphrase identification problem. It represents an entry as a natural language statement by combining multiple types of information contained in it. Then, it predicts the semantic similarity between the natural language statement and the statements mentioning events in scientific literature using a long short-term memory recurrent neural network (LSTM). An event will be normalized to the entry if the two statements are paraphrase. To the best of our knowledge, this is the first attempt of event normalization in the biomedical text mining. The experiments have been conducted using the molecular interaction data from IntAct. The results demonstrate that the method could achieve F-score of 0.87 in normalizing event-containing statements. Availability and implementation The source code is available at the gitlab repository https://gitlab.com/BioAI/leen and BioASQvec Plus is available on figshare https://figshare.com/s/45896c31d10c3f6d857a.

scholarly journals NeuroNLP: a natural language portal for aggregated fruit fly brain data

2016 ◽  
Author(s):  
Nikul H. Ukani ◽  
Adam Tomkins ◽  
Chung-Heng Yeh ◽  
Wesley Bruning ◽  
Allison L. Fenichel ◽  
...  
Keyword(s):  
Natural Language ◽  
3D Visualization ◽  
Fruit Fly ◽  
Multiple Sources ◽  
Web Based ◽  
Brain Circuits ◽  
Complex Circuits ◽  
Brain Data ◽  
Biological Circuits ◽  
The Impact

SummaryNeuroNLP, is a key application on the Fruit Fly Brain Observatory platform (FFBO, http://fruitflybrain.org), that provides a modern web-based portal for navigating fruit fly brain circuit data. Increases in the availability and scale of fruit fly connectome data, demand new, scalable and accessible methods to facilitate investigation into the functions of the latest complex circuits being uncovered. NeuroNLP enables in-depth exploration and investigation of the structure of brain circuits, using intuitive natural language queries that are capable of revealing the latent structure and information, obscured due to expansive yet independent data sources. NeuroNLP is built on top of a database system call NeuroArch that codifies knowledge about the fruit fly brain circuits, spanning multiple sources. Users can probe biological circuits in the NeuroArch database with plain English queries, such as “show glutamatergic local neurons in the left antennal lobe” and “show neurons with dendrites in the left mushroom body and axons in the fan-shaped body”. This simple yet powerful interface replaces the usual, cumbersome checkboxes and dropdown menus prevalent in today’s neurobiological databases. Equipped with powerful 3D visualization, NeuroNLP standardizes tools and methods for graphical rendering, representation, and manipulation of brain circuits, while integrating with existing databases such as the FlyCircuit. The userfriendly graphical user interface complements the natural language queries with additional controls for exploring the connectivity of neurons and neural circuits. Designed with an open-source, modular structure, it is highly scalable/flexible/extensible to additional databases or to switch between databases and supports the creation of additional parsers for other languages. By supporting access through a web browser from any modern laptop or smartphone, NeuroNLP significantly increases the accessibility of fruit fly brain data and improves the impact of the data in both scientific and educational exploration.

Interpreting Natural Language

1987 ◽  
Vol 32 (1) ◽  
pp. 33-34
Author(s):  
Greg N. Carlson
Keyword(s):  
Natural Language
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