scholarly journals Relações semânticas em ontologias: estudo de caso do Blood Project | Semantic relations on ontologies: the case study of the Blood Project

2010 ◽  
Vol 6 (2) ◽  
Author(s):  
Maurício Barcellos Almeida ◽  
Lívia M.D.Teixeira ◽  
Kátia Cardoso Coelho ◽  
Renato R. Souza
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Development Process ◽  
Information Science ◽  
Information Organization ◽  
Semantic Relations ◽  
Medical Texts ◽  
Automatic Tool ◽  
The Impact

Resumo Nos últimos anos, a pesquisa em ontologias tem recebido destaque pelas possibilidades que oferece na organização da informação. No desenvolvimento de ontologias, a fase de conceitualização merece atenção especial por sua importância e complexidade. Esse artigo busca investigar possibilidades de melhorias na fase de conceitualização, adotando aportes da Linguística para verificar variações na semântica das relações entre termos. Apresenta-se uma proposta que abrange: i) um esquema linguístico para identificar relações semânticas em um texto; ii) o uso de uma ferramenta automática para extração de termos de textos médicos; iii) uma avaliação realizada por médicos sobre quais relações seriam mais adequadas. Investiga-se a existência de variações significativas na semântica das relações, o impacto dessa variação no desenvolvimento de ontologias e a validade das relações obtidas. Espera-se contribuir através de novas possibilidades na construção de instrumentos de organização da informação, bem como fornecendo alternativas para os profissionais envolvidos.Palavras-chave ontologias, Ciência da Informação, processamento de linguagem natural, relações semânticas, organização da informação.Abstract In the last years, research on onthologies has received much attention because of the possibilities it offers regarding information organization. During the onthology development process, the phase of conceptualization deserves special attention on account of its importance and complexity. This paper investigates possible improvements in the phase of conceptualization, relying on linguistics theories to verify variations of semantic relations among terms. We present a proposal including: i) a linguistic-based schema which identifies semantic relations in texts; ii) the use of an automatic tool which extracts terms from medical texts; iii) an assessment conducted with physicians requesting which relations are more appropriate. We investigate the possibility of significant variations in the semantic of those relations, the impact of such variation on the development of onthologies and the validity of the obtained relations. We hope to offer new possibilities regarding the construction of information organization instruments, as well as providing alternatives to involved professionals.Keywords onthologies, Information Science, natural language processing, semantic relations, information organization

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.

scholarly journals Exploring Factors Influencing Scenarios Evolution of Waste NIMBY Crisis: Analysis of Typical Cases in China

2021 ◽  
Vol 18 (4) ◽  
pp. 2006
Author(s):  
Ling He ◽  
Qing Yang ◽  
Xingxing Liu ◽  
Lingmei Fu ◽  
Jinmei Wang
Keyword(s):  
Network Theory ◽  
Development Process ◽  
Dynamic Bayesian Network ◽  
Impact Factors ◽  
Social Stability ◽  
Ground Theory ◽  
Evolution Path ◽  
Dynamic Scenario ◽  
The Impact

As the impact factors of the waste Not-In-My-Back Yard (NIMBY) crisis are complex, and the scenario evolution path of it is diverse. Once the crisis is not handled properly, it will bring adverse effects on the construction of waste NIMBY facilities, economic development and social stability. Consequently, based on ground theory, this paper takes the waste NIMBY crisis in China from 2006 to 2019 as typical cases, through coding analysis, scenario evolution factors of waste NIMBY crisis are established. Furtherly, three key scenarios were obtained, namely, external situation (E), situation state (S), emergency management (M), what is more, scenario evolution law of waste NIMBY crisis is revealed. Then, the dynamic Bayesian network theory is used to construct the dynamic scenario evolution network of waste NIMBY crisis. Finally, based on the above models, Xiantao waste NIMBY crisis is taken as a case study, and the dynamic process of scenario evolution network is visually displayed by using Netica. The simulation results show that the scenario evolution network of Xiantao waste NIMBY crisis is basically consistent with the actual incident development process, which confirms the effectiveness and feasibility of the model.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Manifestations ◽  
Predictive Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

scholarly journals Word Embedding for Semantically Relative Words: an Experimental Study

2018 ◽  
Vol 25 (6) ◽  
pp. 726-733
Author(s):  
Maria S. Karyaeva ◽  
Pavel I. Braslavski ◽  
Valery A. Sokolov
Keyword(s):  
Experimental Study ◽  
Natural Language Processing ◽  
Language Processing ◽  
Intelligent Systems ◽  
Russian Language ◽  
Word Embedding ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
Semantic Relationships ◽  
The Russian Language

The ability to identify semantic relations between words has made a word2vec model widely used in NLP tasks. The idea of word2vec is based on a simple rule that a higher similarity can be reached if two words have a similar context. Each word can be represented as a vector, so the closest coordinates of vectors can be interpreted as similar words. It allows to establish semantic relations (synonymy, relations of hypernymy and hyponymy and other semantic relations) by applying an automatic extraction. The extraction of semantic relations by hand is considered as a time-consuming and biased task, requiring a large amount of time and some help of experts. Unfortunately, the word2vec model provides an associative list of words which does not consist of relative words only. In this paper, we show some additional criteria that may be applicable to solve this problem. Observations and experiments with well-known characteristics, such as word frequency, a position in an associative list, might be useful for improving results for the task of extraction of semantic relations for the Russian language by using word embedding. In the experiments, the word2vec model trained on the Flibusta and pairs from Wiktionary are used as examples with semantic relationships. Semantically related words are applicable to thesauri, ontologies and intelligent systems for natural language processing.

scholarly journals Natural Language Processing (NLP) and Its Impact across Industries – Unlocking the True Potential of Digital Healthcare (A Case Study Approach)

2021 ◽  
pp. 86-98
Author(s):  
Sourajit Roy ◽  
Pankaj Pathak ◽  
S. Nithya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Models ◽  
M2m Communication ◽  
Digital Revolution ◽  
Language Complexity ◽  
Study Approach ◽  
Data Variability

During the advent of the 21st century, technical breakthroughs and developments took place. Natural Language Processing or NLP is one of their promising disciplines that has been increasingly dynamic via groundbreaking findings on most computer networks. Because of the digital revolution the amounts of data generated by M2M communication across devices and platforms such as Amazon Alexa, Apple Siri, Microsoft Cortana, etc. were significantly increased. This causes a great deal of unstructured data to be processed that does not fit in with standard computational models. In addition, the increasing problems of language complexity, data variability and voice ambiguity make implementing models increasingly harder. The current study provides an overview of the potential and breadth of the NLP market and its acceptance in industry-wide, in particular after Covid-19. It also gives a macroscopic picture of progress in natural language processing research, development and implementation.

scholarly journals Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Clifford Nangle ◽  
Stuart McTaggart ◽  
Margaret MacLeod ◽  
Jackie Caldwell ◽  
Marion Bennie
Keyword(s):  
Information System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Drug Exposure ◽  
Drug Dose ◽  
Free Text ◽  
Wide Range ◽  
The Impact ◽  
Prescribing Information

ABSTRACT ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.

scholarly journals Comparison of Templates with Word2vec in Finding Semantic Relations Between Words

2018 ◽  
pp. 13-17
Author(s):  
Kaan Ant ◽  
Ugur Sogukpinar ◽  
Mehmet Fatif Amasyali
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Relations ◽  
Template Method ◽  
Semantic Relationships ◽  
Semantic Spaces

The use of databases those containing semantic relationships between words is becoming increasingly widespread in order to make natural language processing work more effective. Instead of the word-bag approach, the suggested semantic spaces give the distances between words, but they do not express the relation types. In this study, it is shown how semantic spaces can be used to find the type of relationship and it is compared with the template method. According to the results obtained on a very large scale, while is_a and opposite are more successful for semantic spaces for relations, the approach of templates is more successful in the relation types at_location, made_of and non relational.

scholarly journals Alumni Reflection To The Experience Of Writing Thesis: Case Study Of Alumni Program In Library And Information Studies Brawijaya University

2021 ◽  
Vol 11 (2) ◽  
pp. 101
Author(s):  
Trimo Septiono
Keyword(s):  
Information Literacy ◽  
Learning Process ◽  
Information Science ◽  
Case Study Research ◽  
Literacy Skills ◽  
Independent Learning ◽  
Study Program ◽  
Thesis Writing ◽  
The Impact

The study aims to describe and identify aspects during the thesis writing process. This study uses a qualitative approach with the type of case study research on alumni of the Library and Information Science Study Program Universitas Brawijaya. Then the selection of alumni as informants considers the impact after writing the thesis is completed. The results showed that the reflection of alumni there is a learning process in each stage of thesis writing. The learning process that occurs is quite complex because it is not only done independently but also involves other parties. Independent learning focuses on the process of applying information literacy skills possessed by alumni. Where there is an information management process which is the most important part, because the process affects the decision making for each action. Meanwhile, the involvement of other parties is another role of the thesis as a forum for collaborative learning. Not only that, the whole process forms a new understanding that can be useful across generations.Keywords: Thesis, Reflection, Information Literacy, Knowledge Sharing Practice, Collaborative LearningABSTRAKRefleksi pengalaman alumni merupakan kajian yang bertujuan untuk mendeskripsikan dan mengidentifikasi aspek-aspek selama proses penulisan skripsi. Penelitian ini menggunakan pendekatan kualitatif dengan jenis penelitian studi kasus pada alumni Program Studi Ilmu Perpustakaan dan Informasi Universitas Brawijaya. Kemudian pemilihan alumni sebagai informan mempertimbangkan dampak yang ditimbulkan pasca penulisan skripsi selesai. Hasil penelitian menunjukkan bahwa refleksi alumni terdapat proses pembelajaran dalam setiap tahapan penulisan skripsi. Proses pembelajaran yang terjadi tergolong kompleks karena tidak hanya dilakukan mandiri tetapi juga melibatkan pihak lain. Pembelajaran secara mandiri menitikberatkan pada proses penerapan kemampuan literasi informasi yang dimiliki oleh alumni. Dimana terdapat proses pengelolaan informasi yang merupakan bagian terpenting, karena proses tersebut berpengaruh pada penentuan keputusan untuk setiap tindakan. Sedangkan untuk pelibatan pihak lain merupakan peran lain dari skripsi sebagai wadah kolaborasi pembelajaran. Tidak hanya itu, secara menyeluruh proses tersebut membentuk pemahaman baru yang dapat bermanfaat lintas generasi.

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