Automatic SNOMED CT coding of Chinese clinical terms via attention-based semantic matching

Objective methods for competence evaluating of scientists in the subject domain pertinent to the specific scientific product – research project, publication, etc. are proposed. These methods are based on the semantic matching of the description of scientific product and documents that confirm the competence of its authors or experts in the domain of this product. In addition, the use of knowledge acquired from the Web open environment – Wiki-resources, scientometric databases, organization official website, domain ontologies is proposed. Specialized ontology of scientific activity which allows to standardize the terminological base for describing the qualifications of researchers is developed.

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FEATURES OF THE TRANSLATION OF V. MAYAKOVSKY'S POEMS INTO PERSIAN

RSUH/RGGU Bulletin. "Literary Theory. Linguistics. Cultural Studies" Series ◽

10.28995/2686-7249-2020-9-53-61 ◽

2020 ◽

pp. 53-61

Author(s):

Nahid Akbarzadeh ◽

Keyword(s):

Comparative Research ◽

Semantic Matching ◽

Vladimir Mayakovsky

This article is dedicated to the translation of poems written by the popular and famous Russian poet Vladimir Mayakovsky into Persian, as well as to their features and general trends of the translation of Futurist poems. The work was performed in the technique of comparative research, where one of the most acute issues is formulated as the degree of translatability / untranslatability of the text. The purpose is to find appropriate approaches for its solution and consider the issue of semantic matching.

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Visual and auditory verbal working memory affects visual attention in the semantic matching

Acta Psychologica Sinica ◽

10.3724/sp.j.1041.2018.00483 ◽

2018 ◽

Vol 50 (5) ◽

pp. 483 ◽

Cited By ~ 3

Keyword(s):

Working Memory ◽

Visual Attention ◽

Verbal Working Memory ◽

Semantic Matching

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Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

Recent Patents on Computer Science ◽

10.2174/2213275912666190710152709 ◽

2019 ◽

Vol 12 ◽

Author(s):

Seema Rani ◽

Avadhesh Kumar ◽

Naresh Kumar

Keyword(s):

Question Answering ◽

Neural Model ◽

Manhattan Distance ◽

Semantic Matching ◽

English Only ◽

Detection Model ◽

Proposed Model ◽

Reported Study ◽

Social Media Platforms ◽

Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

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The use of a Domain Ontology for the Management of Essential Hypertension (Preprint)

10.2196/preprints.25427 ◽

2020 ◽

Author(s):

Emma Chavez ◽

Vanessa Perez ◽

Angélica Urrutia

Keyword(s):

Decision Making ◽

Essential Hypertension ◽

Medical History ◽

Medical Records ◽

Medical Training ◽

Relevant Information ◽

Domain Ontology ◽

Free Text ◽

Snomed Ct ◽

History Of

BACKGROUND : Currently, hypertension is one of the diseases with greater risk of mortality in the world. Particularly in Chile, 90% of the population with this disease has idiopathic or essential hypertension. Essential hypertension is characterized by high blood pressure rates and it´s cause is unknown, which means that every patient might requires a different treatment, depending on their history and symptoms. Different data, such as history, symptoms, exams, etc., are generated for each patient suffering from the disease. This data is presented in the patient’s medical record, in no order, making it difficult to search for relevant information. Therefore, there is a need for a common, unified vocabulary of the terms that adequately represent the diseased, making searching within the domain more effective. OBJECTIVE The objective of this study is to develop a domain ontology for essential hypertension , therefore arranging the more significant data within the domain as tool for medical training or to support physicians’ decision making will be provided. METHODS The terms used for the ontology were extracted from the medical history of de-identified medical records, of patients with essential hypertension. The Snomed-CT’ collection of medical terms, and clinical guidelines to control the disease were also used. Methontology was used for the design, classes definition and their hierarchy, as well as relationships between concepts and instances. Three criteria were used to validate the ontology, which also helped to measure its quality. Tests were run with a dataset to verify that the tool was created according to the requirements. RESULTS An ontology of 310 instances classified into 37 classes was developed. From these, 4 super classes and 30 relationships were obtained. In the dataset tests, 100% correct and coherent answers were obtained for quality tests (3). CONCLUSIONS The development of this ontology provides a tool for physicians, specialists, and students, among others, that can be incorporated into clinical systems to support decision making regarding essential hypertension. Nevertheless, more instances should be incorporated into the ontology by carrying out further searched in the medical history or free text sections of the medical records of patients with this disease.

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Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01495-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pilar López-Úbeda ◽

Alexandra Pomares-Quimbaya ◽

Manuel Carlos Díaz-Galiano ◽

Stefan Schulz

Keyword(s):

Language Processing ◽

Classification Problem ◽

Snomed Ct ◽

Language Resources ◽

Clinical Specialty ◽

Controlled Vocabularies ◽

Clinical Text ◽

Domain Specific ◽

Medical Terms ◽

Core Vocabulary

Abstract Background Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong. Results This paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks. Conclusion The creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.

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Single-shot Semantic Matching Network for Moment Localization in Videos

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3441577 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-14

Author(s):

Xinfang Liu ◽

Xiushan Nie ◽

Junya Teng ◽

Li Lian ◽

Yilong Yin

Keyword(s):

Natural Language ◽

Fixed Number ◽

Single Shot ◽

Semantic Matching ◽

Matching Network ◽

Attention Model ◽

Semantic Relationships ◽

Natural Language Query ◽

Benchmark Datasets ◽

Short Memory

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.

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Semantic Matching for Short Texts: A Cross Attention Mechanism

Journal of Physics Conference Series ◽

10.1088/1742-6596/1757/1/012087 ◽

2021 ◽

Vol 1757 (1) ◽

pp. 012087

Author(s):

Senhong Zheng ◽

Fan Chen ◽

Xuejie Wang

Keyword(s):

Attention Mechanism ◽

Semantic Matching

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