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Register a new userIntroducing Information Retrieval for Biomedical Informatics Students
تقديم استرجاع المعلومات لطلاب المعلوماتية الطبية الحيوية
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إن إدخال طلاب المعلوماتيين الطبية الحيوية (BMI) للمعالجة اللغوية الطبيعية (NLP) يتطلب موازنة العمق الفني مع المعرفة العملية لمعالجة الاحتياجات التي تركز على التطبيق.قمنا بتطوير مجموعة من ثلاثة أنشطة إدخال طلاب BMI تمهيدي لاسترجاع المعلومات مع NLP، والغطاء استراتيجيات تمثيل المستندات ونماذج اللغة من TF-IDF إلى Bert.تزود هذه الأنشطة الطلاب بتجربة عملية مستهدفة نحو حالات الاستخدام المشترك، وإدخال مكونات أساسية من سير العمل NLP لمجموعة واسعة من التطبيقات.
Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs. We developed a set of three activities introducing introductory BMI students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT. These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.
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We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs
machine translation of source-language queries into a document language and linear regression to predict the retriev
al performance for each translated query when expanded with a candidate term.
Candidate terms (in the document language) come from multiple sources: query translation hypotheses obtained from the machine translation system, Wikipedia articles and PubMed abstracts. Query expansion is applied only when the model predicts a score for a candidate term that exceeds a tuned threshold which allows to expand queries with strongly related terms only.
Our experiments are conducted using the CLEF eHealth 2013--2015 test collection and show %seven source languages and also in the monolingual case. The results show
significant improvements in both cross-lingual and monolingual settings.
The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential ex
pression testing and pathway analysis with the results obtained informing future experiments and validation studies
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks
such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.
In this paper we present the FJWU's system submitted to the biomedical shared task at WMT21. We prepared state-of-the-art multilingual neural machine translation systems for three languages (i.e. German, Spanish and French) with English as target lan
guage. Our NMT systems based on Transformer architecture, were trained on combination of in-domain and out-domain parallel corpora developed using Information Retrieval (IR) and domain adaptation techniques.
This research proposes a new way to improve the
search outcome of Arabic semantics by abstractly summarizing the
Arabic texts (Abstractive Summary) using natural language
processing algorithms(NLP),Word Sense Disambiguation (WSD)
and techniques o
f measuring Semantic Similarity in Arabic WordNet
Ontology.
معالجة اللغات الطبيعية
Semantic analysis
استرجاع المعلومات
التلخيص التجريدي
الأنتولوجيا العربية ووردنت
العلاقة الدلالية المفاهيمية
التشابهية الدلالية
التحليل الدلالي
حل غموض معاني الكلمات
(Natural Language Processing (NLP
(Information Retrieval (IR
Abstractive Summarization
(Arabic WordNet (AWN
Conceptual Semantic Relation
Semantic Similarity
(Word Sense Disambiguation (WSD
المزيد..
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