scholarly journals Identificação de Pragas e Doenças na Cultura da Soja por meio de um Sistema Computacional em Linguagem Natural

2021 ◽  
Author(s):  
Carolinne Roque e Faria ◽  
Cinthyan Renata Sachs Camerlengo de Barb
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer System ◽  
Language Processing ◽  
Agricultural Area ◽  
Syntactic Analysis ◽  
Dependency Parsing ◽  
Named Entities ◽  
Pests And Diseases ◽  
Improve Production

Technology is becoming expressively popular among agribusiness producers and is progressing in all agricultural area. One of the difficulties in this context is to handle data in natural language to solve problems in the field of agriculture. In order to build up dialogs and provide rich researchers, the present work uses Natural Language Processing (NLP) techniques to develop an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in the soybean farming, stored in a database repository to provide accurate diagnoses to simplify the work of the agricultural professional and also for those who deal with a lot of information in this area. Information on 108 pests and 19 diseases that damage Brazilian soybean was collected from Brazilian bibliographic manuals with the purpose to optimize the data and improve production, using the spaCy library for syntactic analysis of NLP, which allowed the pre-process the texts, recognize the named entities, calculate the similarity between the words, verify dependency parsing and also provided the support for the development requirements of the CAROLINA tool (Robotized Agronomic Conversation in Natural Language) using the language belonging to the agricultural area.

scholarly journals System for Identifying Pests and Diseases in Soybean Crop through Natural Language Processing

2022 ◽  
Vol 29 (1) ◽  
pp. 28-41
Author(s):  
Carolinne Roque e Faria ◽  
Cinthyan S. C. Barbosa
Keyword(s):  
Natural Language Processing ◽  
Data Structure ◽  
Natural Language ◽  
Computer System ◽  
Language Processing ◽  
Relational Database ◽  
Soybean Cultivar ◽  
Soybean Crop ◽  
Pests And Diseases ◽  
Processing Techniques

The presence of technologies in the agronomic field has the purpose of proposing the best solutions to the challenges found in agriculture, especially to the problems that affect cultivars. One of the obstacles found is to apply the use of your own language in applications that interact with the user in Brazilian Agribusiness. Therefore, this work uses Natural Language Processing techniques for the development of an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in soybean crop, stored in a non-relational database repository to provide accurate diagnostics to simplify the work of the farmer and the agricultural stakeholders who deal with a lot of information. In order to build dialogues and provide rich consultations, from agriculture manuals, a data structure with 108 pests and diseases with their information on the soybean cultivar and through the spaCy tool, it was possible to pre-process the texts, recognize the entities and support the requirements for the development of the conversacional system.

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.

scholarly journals Basic challenges in natural language processing systems

2020 ◽  
pp. 41-45
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Search ◽  
Question Answering ◽  
Syntactic Analysis ◽  
Anaphora Resolution ◽  
Grammatical Structure ◽  
English Sentence ◽  
Improved Model

The article proceeds from the intended use of parsing for the purposes of automatic information search, question answering, logical conclusions, authorship verification, text authenticity verification, grammar check, natural language synthesis and other related tasks, such as ungrammatical speech analysis, morphological class definition, anaphora resolution etc. The study covers natural language processing challenges, namely of an English sentence. The article describes formal and linguistic 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 today. 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 examine the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them. The analysis identifies a number of linguistic issues that will contribute to the development of an improved model of automatic syntactic analysis: lexical and grammatical synonymy and homonymy, hypo- and hyperonymy, lexical and semantic fields, anaphora resolution, ellipsis, inversion etc. The scope of natural language processing reveals obvious directions for the improvement of parsing models. The improvement will consequently expand the scope and improve the results in areas that already employ automatic parsing. Indispensable achievements in vocabulary and morphology processing shall not be neglected while improving automatic syntactic analysis mechanisms for natural languages.

scholarly journals Critique on Cache Transition Techniques for Semantic Graph Parsing for optimizing Search Process using Text Mining

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1014-1018
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Vital Role ◽  
Transition System ◽  
Search Process ◽  
Dependency Parsing ◽  
Fixed Size ◽  
Decomposition Theory

This paper elaborates the transition system that gives the standard transition-based dependency parsing techniques for generating the graph. It is essential to know the standard transition techniques for all graphical problems. Cache transition technique plays a vital role in optimizing the search process in various text mining applications. This paper provides an overview on cache transition technique for parsing semantic graphs for several Natural Language Processing (NLP) applications. According to this paper, the cache is having the fixed size m, by tree decomposition theory according to which there is a relationship between the parameter m and class of graphs produced by the theory.

Extraction of Construction Quality Requirements from Textual Specifications via Natural Language Processing

2021 ◽  
pp. 036119812110013
Author(s):  
JungHo Jeon ◽  
Xin Xu ◽  
Yuxi Zhang ◽  
Liu Yang ◽  
Hubo Cai
Keyword(s):  
Neural Network ◽  
South Carolina ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Analysis ◽  
Test Case ◽  
Promising Alternative ◽  
Construction Inspection ◽  
Construction Specification

Construction inspection is an essential component of the quality assurance programs of state transportation agencies (STAs), and the guidelines for this process reside in lengthy textual specifications. In the current practice, engineers and inspectors must manually go through these documents to plan, conduct, and document their inspections, which is time-consuming, very subjective, inconsistent, and prone to error. A promising alternative to this manual process is the application of natural language processing (NLP) techniques (e.g., text parsing, sentence classification, and syntactic analysis) to automatically extract construction inspection requirements from textual documents and present them as straightforward check questions. This paper introduces an NLP-based method that: 1) extracts individual sentences from the construction specification; 2) preprocesses the resulting sentences; 3) applies Word2Vec and GloVe algorithms to extract vector features; 4) uses a convolutional neural network (CNN) and recurrent neural network to classify sentences; and 5) converts the requirement sentences into check questions via syntactic analysis. The overall methodology was assessed using the Indiana Department of Transportation (DOT) specification as a test case. Our results revealed that the CNN + GloVe combination led to the highest accuracy, at 91.9%, and the lowest loss, at 11.7%. To further validate its use across STAs nationwide, we applied it to the construction specification of the South Carolina DOT as a test case, and our average accuracy was 92.6%.

A Principle-Based System for Syntactic Analysis

1991 ◽  
Vol 36 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Matthew W. Crocker
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Ad Hoc ◽  
Syntactic Analysis ◽  
Large Sets ◽  
Transition Network ◽  
Traditional Approaches

Traditional approaches to natural language processing (NLP) can be considered construction-based. That is to say, they employ surface oriented, language specific rules, whether in the form of an Augmented Transition Network (ATN), logic grammar or some other grammar/parsing formalism. The problems of such approaches have always been apparent; they involve large sets of rules, often ad hoc, and their adequacy with respect to the grammar of the language is difficult to ensure.

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.

SmartShell: Automated Shell Scripts Synthesis from Natural Language

2019 ◽  
Vol 29 (02) ◽  
pp. 197-220
Author(s):  
Hao Li ◽  
Yu-Ping Wang ◽  
Jie Yin ◽  
Gang Tan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer System ◽  
Language Processing ◽  
Syntax Tree ◽  
Intermediate Language ◽  
System Administration ◽  
Shell Script ◽  
Rich Functionality ◽  
Short Shell

Modern shell scripts provide interfaces with rich functionality for system administration. However, it is not easy for end-users to write correct shell scripts; misusing commands may cause unpredictable results. In this paper, we present SmartShell, an automated function-based tool for shell script synthesis, which uses natural language descriptions as input. It can help the computer system to “understand” users’ intentions. SmartShell is based on two insights: (1) natural language descriptions for system objects (such as files and processes) and operations can be recognized by natural language processing tools; (2) system-administration tasks are often completed by short shell scripts that can be automatically synthesized from natural language descriptions. SmartShell synthesizes shell scripts in three steps: (1) using natural language processing tools to convert the description of a system-administration task into a syntax tree; (2) using program-synthesis techniques to construct a SmartShell intermediate-language script from the syntax tree; (3) translating the intermediate-language script into a shell script. Experimental results show that SmartShell can successfully synthesize 53.7% of tasks collected from shell-script helping forums.

A Multilevel Natural Language Processing Model

1988 ◽  
Vol 11 (1-2) ◽  
pp. 69-87 ◽  
Author(s):  
H. Jäppinen ◽  
T. Honkela ◽  
H. Hyötyniemi ◽  
A. Lehtola
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Multilevel Model ◽  
Morphological Analysis ◽  
Semantic Analysis ◽  
Dependency Parsing ◽  
Computational Aspects

In this paper we describe a multilevel model for natural language processing. The distinct computational strata are motivated by invariant linguistic properties which are progressively uncovered from utterances. We examine each level in detail. The processes are morphological analysis, dependency parsing, logico-semantic analysis and query adaptation. Both linguistic and computational aspects are discussed. In addition to theory, we consider certain engineering viewpoints important and discuss them briefly.

scholarly journals Review on Natural Language Processing Based on Different Techniques

2021 ◽  
pp. 1-17
Author(s):  
Dastan Hussen Maulud ◽  
Siddeeq Y. Ameen ◽  
Naaman Omar ◽  
Shakir Fattah Kak ◽  
Zryan Najat Rashid ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Good Accuracy ◽  
Syntactic Analysis ◽  
High Demand ◽  
Syntax Analysis ◽  
Language Analysis ◽  
Third Stage ◽  
And Control

With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error. The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.

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