scholarly journals PEMBANGKIT ENTITY RELATIONSHIP DIAGRAM DARI SPESIFIKASI KEBUTUHAN MENGGUNAKAN NATURAL LANGUAGE PROCESSING UNTUK BAHASA INDONESIA

2021 ◽  
Vol 9 (2) ◽  
pp. 196-206
Author(s):  
Parmonangan R. Togatorop ◽  
Rezky Prayitno Simanjuntak ◽  
Siti Berliana Manurung ◽  
Mega Christy Silalahi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rule Based ◽  
Pos Tagging ◽  
Sentence Segmentation ◽  
Primary Key ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Bahasa Indonesia

Memodelkan Entity Relationship Diagram (ERD) dapat dilakukan secara manual, namun umumnya memperoleh pemodelan ERD secara manual membutuhkan waktu yang lama. Maka, dibutuhkan pembangkit ERD dari spesifikasi kebutuhan untuk mempermudah dalam melakukan pemodelan ERD. Penelitian ini bertujuan untuk mengembangkan sebuah sistem pembangkit ERD dari spesifikasi kebutuhan dalam Bahasa Indonesia dengan menerapkan beberapa tahapan-tahapan dari Natural Language Processing (NLP) sesuai kebutuhan penelitian. Spesifikasi kebutuhan yang digunakan tim peneliti menggunakan teknik document analysis. Untuk tahapan-tahapan dari NLP yang digunakan oleh peneliti yaitu: case folding, sentence segmentation, tokenization, POS tagging, chunking dan parsing. Kemudian peneliti melakukan identifikasi terhadap kata-kata dari teks yang sudah diproses pada tahapan-tahapan dari NLP dengan metode rule-based untuk menemukan daftar kata-kata yang memenuhi dalam komponen ERD seperti: entitas, atribut, primary key dan relasi. ERD kemudian digambarkan menggunakan Graphviz berdasarkan komponen ERD yang telah diperoleh Evaluasi hasil ERD yang berhasil dibangkitkan kemudian di evaluasi  menggunakan metode evaluasi expert judgement. Dari hasil evaluasi berdasarkan beberapa studi kasus diperoleh hasil rata-rata precision, recall, F1 score berturut-turut dari tiap ahli yaitu: pada ahli 1 diperoleh 91%, 90%, 90%; pada ahli 2 diperoleh 90%, 90%, 90%; pada ahli 3 diperoleh 98%, 94%, 96%; pada ahli 4 diperoleh 93%, 93%, 93%; dan pada ahli 5 diperoleh 98%, 83%, 90%.

scholarly journals IMPLEMENTASI NATURAL LANGUAGE PROCESSING (NLP) UNTUK APLIKASI PENCARIAN LOKASI

2021 ◽  
Vol 3 (2) ◽  
pp. 15
Author(s):  
Irkham Huda
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Pos Tagging ◽  
Bahasa Indonesia

Pencarian lokasi menjadi salah satu kebutuhan masyarakat dewasa ini terbukti dengan banyaknya penyedia layanan pemetaan. Untuk mencari lokasi dengan referensi relasi spasial tertentu, pengguna mendeskripsikannya dengan bahasa natural. Maka untuk membuat sistem pencarian lokasi yang mampu memahami masukan pengguna diperlukan implementasi Natural Language Processing (NLP). Penelitian terkait implementasi NLP untuk aplikasi pencarian lokasi masih dirasa perlu terutama karena belum adanya implementasi penelitian tersebut yang mendukung Bahasa Indonesia, sedangkan penelitian terkait yang sudah ada hanya mendukung Bahasa Inggris dengan cakupan terbatas.Dalam penelitian ini dikembangkan Sistem NLP untuk Aplikasi Pencarian Lokasi dikenal dengan NaLaMap. Basis data lokasi yang dimanfaatkan adalah Open Street Map (OSM) dan digunakan aplikasi web sebagai client untuk studi kasus. Dalam mentransformasikan kalimat masukan pencarian lokasi menjadi query spasial, Sistem NLP yang dibangun melalui lima tahapan utama yaitu Tokenisasi, POS Tagging, NER Tagging, Normalisasi Entitas, dan Penyusunan Query. Kemudian query yang berhasil disusun dijalankan pada basis data lokasi berbasis OSM sehingga diperoleh hasil pencarian yang akan ditampilkan melalui peta pada aplikasi client.Hasil uji coba sistem secara keseluruhan menggunakan 45 kalimat masukan dari responden, diperoleh hasil yang cukup bagus dengan nilai precision 0,97 dan recall 0,91. 

Triage and diagnosis of COVID-19 from medical social media (Preprint)

2021 ◽  
Author(s):  
Abul Hasan ◽  
Mark Levene ◽  
David Weston ◽  
Renate Fromson ◽  
Nicolas Koslover ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models ◽  
Rule Based ◽  
Additional Information ◽  
Processing Pipeline ◽  
Machine Learning Models

BACKGROUND The COVID-19 pandemic has created a pressing need for integrating information from disparate sources, in order to assist decision makers. Social media is important in this respect, however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. In particular, machine learning techniques for triage and diagnosis could allow for a better understanding of what social media may offer in this respect. OBJECTIVE This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts, in order to provide researchers and other interested parties with additional information on the symptoms, severity and prevalence of the disease. METHODS The text processing pipeline first extracts COVID-19 symptoms and related concepts such as severity, duration, negations, and body parts from patients’ posts using conditional random fields. An unsupervised rule-based algorithm is then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations are subsequently used to construct two different vector representations of each post. These vectors are applied separately to build support vector machine learning models to triage patients into three categories and diagnose them for COVID-19. RESULTS We report that Macro- and Micro-averaged F_{1\ }scores in the range of 71-96% and 61-87%, respectively, for the triage and diagnosis of COVID-19, when the models are trained on human labelled data. Our experimental results indicate that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. Also, we highlight important features uncovered by our diagnostic machine learning models and compare them with the most frequent symptoms revealed in another COVID-19 dataset. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from natural language narratives using a machine learning pipeline, in order to provide additional information on the severity and prevalence of the disease through the eyes of social media.

scholarly journals Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data

2009 ◽  
Vol 16 (4) ◽  
pp. 571-575 ◽  
Author(s):  
L. C. Childs ◽  
R. Enelow ◽  
L. Simonsen ◽  
N. H. Heintzelman ◽  
K. M. Kowalski ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Data ◽  
Rule Based ◽  
Rule Based System

scholarly journals Clinical trial cohort selection based on multi-level rule-based natural language processing system

2019 ◽  
Vol 26 (11) ◽  
pp. 1218-1226 ◽  
Author(s):  
Long Chen ◽  
Yu Gu ◽  
Xin Ji ◽  
Chao Lou ◽  
Zhiyong Sun ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rule Based ◽  
Language System ◽  
Unified Medical Language System ◽  
Rule Based System ◽  
Medical Language ◽  
Cohort Selection

Abstract Objective Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients’ eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials. Materials and Methods The authors developed an integrated rule-based clinical NLP system which employs a generic rule-based framework plugged in with lexical-, syntactic- and meta-level, task-specific knowledge inputs. In addition, the authors also implemented and evaluated a general clinical NLP (cNLP) system which is built with the Unified Medical Language System and Unstructured Information Management Architecture. Results and Discussion The systems were evaluated as part of the 2018 n2c2-1 challenge, and authors’ rule-based system obtained an F-measure of 0.9028, ranking fourth at the challenge and had less than 1% difference from the best system. While the general cNLP system didn’t achieve performance as good as the rule-based system, it did establish its own advantages and potential in extracting clinical concepts. Conclusion Our results indicate that a well-designed rule-based clinical NLP system is capable of achieving good performance on cohort selection even with a small training data set. In addition, the investigation of a Unified Medical Language System-based general cNLP system suggests that a hybrid system combining these 2 approaches is promising to surpass the state-of-the-art performance.

scholarly journals PENENTUAN KELAS KATA PADA PART OF SPEECH TAGGING KATA AMBIGU BAHASA INDONESIA

2018 ◽  
Vol 2 (3) ◽  
pp. 157
Author(s):  
Ahmad Subhan Yazid ◽  
Agung Fatwanto
Keyword(s):  
Language Processing ◽  
Word Class ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Ambiguous Words ◽  
Computer Science Faculty ◽  
Speech Tagging ◽  
Bahasa Indonesia

Indonesian hold a fundamental role in the communication. There is ambiguous problem in its machine learning implementation. In the Natural Language Processing study, Part of Speech (POS) tagging has a role in the decreasing this problem. This study use the Rule Based method to determine the best word class for ambiguous words in Indonesian. This research follows some stages: knowledge inventory, making algorithms, implementation, Testing, Analysis, and Conclusions. The first data used is Indonesian corpus that was developed by Language department of Computer science Faculty, Indonesia University. Then, data is processed and shown descriptively by following certain rules and specification. The result is a POS tagging algorithm included 71 rules in flowchart and descriptive sentence notation. Refer to testing result, the algorithm successfully provides 92 labeling of 100 tested words (92%). The results of the implementation are influenced by the availability of rules, word class tagsets and corpus data.

scholarly journals Improving Brill's tagger lexical and transformation rule for Afaan Oromo language

2015 ◽  
Author(s):  
Abraham G Ayana
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Transformation Rule ◽  
Initial State ◽  
Training Corpus ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Speech Tagging

Natural Language Processing (NLP) refers to Human-like language processing which reveals that it is a discipline within the field of Artificial Intelligence (AI). However, the ultimate goal of research on Natural Language Processing is to parse and understand language, which is not fully achieved yet. For this reason, much research in NLP has focused on intermediate tasks that make sense of some of the structure inherent in language without requiring complete understanding. One such task is part-of-speech tagging, or simply tagging. Lack of standard part of speech tagger for Afaan Oromo will be the main obstacle for researchers in the area of machine translation, spell checkers, dictionary compilation and automatic sentence parsing and constructions. Even though several works have been done in POS tagging for Afaan Oromo, the performance of the tagger is not sufficiently improved yet. Hence,the aim of this thesis is to improve Brill’s tagger lexical and transformation rule for Afaan Oromo POS tagging with sufficiently large training corpus. Accordingly, Afaan Oromo literatures on grammar and morphology are reviewed to understand nature of the language and also to identify possible tagsets. As a result, 26 broad tagsets were identified and 17,473 words from around 1100 sentences containing 6750 distinct words were tagged for training and testing purpose. From which 258 sentences are taken from the previous work. Since there is only a few ready made standard corpuses, the manual tagging process to prepare corpus for this work was challenging and hence, it is recommended that a standard corpus is prepared. Transformation-based Error driven learning are adapted for Afaan Oromo part of speech tagging. Different experiments are conducted for the rule based approach taking 20% of the whole data for testing. A comparison with the previously adapted Brill’s Tagger made. The previously adapted Brill’s Tagger shows an accuracy of 80.08% whereas the improved Brill’s Tagger result shows an accuracy of 95.6% which has an improvement of 15.52%. Hence, it is found that the size of the training corpus, the rule generating system in the lexical rule learner, and moreover, using Afaan Oromo HMM tagger as initial state tagger have a significant effect on the improvement of the tagger.

scholarly journals Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia

2021 ◽  
Vol 10 (4) ◽  
pp. 354-361
Author(s):  
Yudi Widhiyasana ◽  
Transmissia Semiawan ◽  
Ilham Gibran Achmad Mudzakir ◽  
Muhammad Randi Noor
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Rate ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.

scholarly journals Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study

10.2196/25157 ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. e25157
Author(s):  
Zhen Yang ◽  
Chloé Pou-Prom ◽  
Ashley Jones ◽  
Michaelia Banning ◽  
David Dai ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Expanded Disability Status Scale ◽  
Health Records ◽  
Rule Based ◽  
Disability Status ◽  
Edss Score ◽  
Electronic Health

Background The Expanded Disability Status Scale (EDSS) score is a widely used measure to monitor disability progression in people with multiple sclerosis (MS). However, extracting and deriving the EDSS score from unstructured electronic health records can be time-consuming. Objective We aimed to compare rule-based and deep learning natural language processing algorithms for detecting and predicting the total EDSS score and EDSS functional system subscores from the electronic health records of patients with MS. Methods We studied 17,452 electronic health records of 4906 MS patients followed at one of Canada’s largest MS clinics between June 2015 and July 2019. We randomly divided the records into training (80%) and test (20%) data sets, and compared the performance characteristics of 3 natural language processing models. First, we applied a rule-based approach, extracting the EDSS score from sentences containing the keyword “EDSS.” Next, we trained a convolutional neural network (CNN) model to predict the 19 half-step increments of the EDSS score. Finally, we used a combined rule-based–CNN model. For each approach, we determined the accuracy, precision, recall, and F-score compared with the reference standard, which was manually labeled EDSS scores in the clinic database. Results Overall, the combined keyword-CNN model demonstrated the best performance, with accuracy, precision, recall, and an F-score of 0.90, 0.83, 0.83, and 0.83 respectively. Respective figures for the rule-based and CNN models individually were 0.57, 0.91, 0.65, and 0.70, and 0.86, 0.70, 0.70, and 0.70. Because of missing data, the model performance for EDSS subscores was lower than that for the total EDSS score. Performance improved when considering notes with known values of the EDSS subscores. Conclusions A combined keyword-CNN natural language processing model can extract and accurately predict EDSS scores from patient records. This approach can be automated for efficient information extraction in clinical and research settings.

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