Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLeveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically
الاستفادة من كبسولة توجيه المعرفة العارضة مع الأدب الطبي هرمي
370
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
إن دمج المعرفة في نص هو وسيلة واعدة لإثراء التمثيل النصي، خاصة في المجال الطبي. ومع ذلك، فإن المعرفة غير المتمايزة لا تخلط بين تمثيل النص فحسب، بل تستورد أيضا ضوضاء غير متوقعة. في هذه الورقة، لتخفيف هذه المشكلة، نقترح الاستفادة من كبسولة التوجيه لربط المعرفة مع الأدب الطبي هرمي (يسمى Hicapsrkl). أولا، تستخرج Hicapsrkl شظايا نصية مصممة تجريبية من الأدب الطبي وتكررها في تمثيلات شظية على التوالي. ثانيا، يتم تطبيق خوارزمية التوجيه الكبسولة على تمثيلين شظيين. من خلال الحوسبة الكبسولة والتوجيه الديناميكي، تتم معالجة كل تمثيل في تمثيل جديد (يشار إليه كتمثيل قبعات)، ونحن ندمج تمثيلات قبعات ككبار معلومات للمعرفة العاملية بالأدب الطبي هرمي. أخيرا، يتم التحقق من صحة Hicapsrkl عند التنبؤ بالأهمية ومجموعات اختبار استرجاع الأدب الطبي. تظهر النتائج والتحليلات التجريبية أن Hicapsrklcan أكثر دقة المعرفة مع الأدب الطبي من الأساليب الرئيسية. باختصار، يمكن أن يساعد Hicapsrkl بكفاءة في اختيار المعرفة الأكثر صلة بالأدب الطبي، والتي قد تكون محاولة بديلة لتحسين تمثيل النص المستند إلى المعرفة. يتم إصدار شفرة المصدر على Github.
Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field. However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises. In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL). Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively. Secondly, the capsule routing algorithm is applied to two fragment representations. Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically. Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets. The experimental results and analyses show that HiCapsRKLcan more accurately associate knowledge with medical literature than mainstream methods. In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation. Source code is released on GitHub.
References used
https://aclanthology.org/
rate research
Read More
In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this pap
er, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.
This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 5
00 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.
Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module. Owing to numerous surface variants of biomedical concepts, BCN still remains challenging and unsolved. In this paper, we exploit biomedical c
oncept hypernyms to facilitate BCN. We propose Biomedical Concept Normalizer with Hypernyms (BCNH), a novel framework that adopts list-wise training to make use of both hypernyms and synonyms, and also employs norm constraint on the representation of hypernym-hyponym entity pairs. The experimental results show that BCNH outperforms the previous state-of-the-art model on the NCBI dataset.
Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of service and reduce costs. However, the complexity of e
lectronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. The results on two prediction tasks show that our fused model with different data outperforms the state-of-the-art method without clinical notes, which illustrates the importance of our fusion method and the clinical note features.
To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive kno
wledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
