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In the project titled "Information Extraction from Free-Text Radiology Reports Using Named Entity Recognition and Relation Extraction", our team aimed to address the challenges associated with the lack of standardized structure in radiology reports, hindering accessibility and efficient utilization. This limitation negatively affects decision-making and patient care, making it essential to find a solution for enhancing interoperability and enabling advanced analytics.
In this research, we adopted a multi-faceted approach using deep learning and large language models to enhance information extraction from Turkish free-text radiology reports. We fine-tuned a BERT model and integrated it into DyGIE++ for named entity recognition (NER), improving understanding of the Turkish language within the radiology domain. Additionally, we trained a Seq2Seq model to simplify complex jargon in reports for better patient comprehension. Leveraging GPT-3.5, we augmented the dataset with multiple simplified versions of each report. The results showed promising improvements in information extraction and patient engagement. Our research lays the groundwork for further advancements in medical AI and NLP in the Turkish Radiology domain.
We would like to express our appreciation to the advisory Radiologists group from Ankara Bilkent City Hospital, led by Assoc. Prof Ural Koc, radiology residents Ceren Aydin, Muhammet Batuhan Gokhan, and Ali Bahadir Ozdemir for their valuable input and support throughout this project. Their collaboration in providing a set of radiology reports and assisting with the annotation work was vital to the success of our research.
Lastly, we extend our special thanks to NLP researchers Abubakar Ahmad Abdullahi and Gıyaseddin Bayrak from our lab for their significant contributions and assistance in the project. Their expertise and guidance were invaluable in achieving the impressive results we have obtained.
Stay tuned for more innovative and transformative updates from our lab in the field of NLP and AI. If you have any questions or are curious about our project, please don't hesitate to reach out!
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NER plays a crucial role in identifying and classifying named entities like people, places, and medical terms. In our research project, we are dedicated to enhancing NER in Turkish-language radiology reports. To achieve this, we will fine-tune and test various language models, including BERT, GPT-2, GPT-3.5, and GPT-4, using a substantial corpus of Turkish radiology reports.
By refining these language models, we aim to improve their performance on NER and explore the potential of advanced models for simplifying Turkish radiology reports. This research has the power to advance natural language processing in the medical domain, benefiting Turkish-speaking populations and ultimately leading to enhanced patient care and outcomes.
Stay tuned for updates on our groundbreaking research in medical NLP for Turkish-language radiology reports!
linkedin.com/posts/bigdata-lab
A knowledge graph is a semantic graph structure that enables us to visualize and understand the connections and similarities between different fields of cases. By analyzing a dataset of 13,990 thesis documents in JSON format, containing valuable information like keywords, authors, and text, our senior students are utilizing the keywords field to construct the knowledge graph.
Considering word frequencies, they made adjustments to efficiently extract keywords using regexes and replace functions. For text classification and representation learning, they harnessed the power of fastText, an efficient library capable of handling large datasets and out-of-vocabulary words. Additionally, they utilized a fine-tuned version of fastText @BIGDaTA_Lab , enabling them to better understand legal terms.
Another essential tool in their project is BERT, a powerful language representation model based on transformer architecture. With its contextualized word embeddings, BERT achieves state-of-the-art performance in various NLP tasks. They employed the bert-extractive-summarizer 0.10.1 to summarize case law texts, enhancing efficiency and accessibility.
We are proud of their dedication and innovative approach that have a potential to speed up the intensive search of legal professionals and thus making a significant impact on the legal system.
linkedin.com/posts/bigdata-lab
In the legal field, research can be an arduous task, demanding valuable time and resources from lawyers. That's why our project aims to revolutionize legal research by developing a specialized question and answer system tailored specifically for Turkish Law. Our @BIGDaTA_Lab's cutting-edge system employs advanced Natural Language Processing (NLP) algorithms to provide legal professionals with a simplified, cost-effective, and easily accessible tool for prompt and efficient legal inquiries.
Throughout the project, our team explored various models, and the Bert2Bert model consistently outperformed the T5 model in terms of speed and response times across all iterations. With a higher F1 score, the Bert2Bert model strikes a remarkable balance between precision and recall, ensuring accurate and reliable answers for legal queries. Meanwhile, the T5 model demonstrated superior precision and recall.
This project not only contributes to the development and implementation of question-answering systems in the Turkish legal domain but also lays the foundation for efficient analysis of Turkish legal texts by legal professionals and researchers. Our efforts are propelling advancements in Turkish NLP and AI research, revolutionizing the way legal research is conducted.
We are excited about the future prospects of our project. Our next steps involve publishing a comprehensive publication. Additionally, we are committed to improving the speed and performance of our web application by implementing necessary enhancements and refining our model. To accomplish this, we will expand our dataset, making it pioneering and extensive in the field of Turkish Law Question-Answer.
We would like to extend our heartfelt gratitude to Cihan Erdoğanyılmaz for his leading work and leadership in Q&A dataset preparation. His expertise and guidance have ensured the quality of our data while navigating the intricacies of legal regulations. We also want to express our sincere appreciation to our Master's student Batuhan Özdöl for his significant contributions in modelling throughout the project and to Rafah Alomar for providing valuable support during the data preparation phase.
Stay tuned for more updates on our remarkable journey as we continue to innovate and transform the landscape of legal research in Turkish Law. If you're interested in learning more about our project or wish to collaborate, please don't hesitate to reach out.
linkedin.com/posts/bigdata-lab
The project addresses the inefficiency of general-purpose search engines and keyword-based searches in the legal domain. It aims to enhance search efficiency and improve the quality of results by developing a domain-specific search engine for law.
The project utilizes several powerful AI models including #fastText, #BERT, and several #sentencetransformer models, to improve the retrieval process and semantic understanding. By leveraging semantic search techniques, the engine can identify both exact matches and results that are semantically related to the query. The experimentation phase compared the performance of various algorithms, highlighting their strengths and weaknesses.
Future plans include expanding the collection of Jurisprudential documents, implementing a Legislation Search Engine, enhancing the user interface, transforming the project into a market-ready product, implementing a serverless search engine on cloud platforms, and reaching a wider user base for valuable feedback to improve the #SBERT models and overall performance. The project expresses excitement about the possibilities and expresses gratitude for the support received with more updates to come. Interested individuals are encouraged to reach out to learn more about the project.
linkedin.com/posts/bigdata-lab
Problem:
With the increasing number of lawsuits, lawyers face a daunting challenge of preparing petitions and legal documents that often follow similar formats but vary in case descriptions. This repetitive and time-consuming process demands significant effort.
Solution:
We are proud to present a novel petition generation tool designed specifically for lawyers as a decision support system. Our tool harnesses the power of language models to streamline the process.
Methodology:
* Semantic Search:
Our approach utilizes the SentenceTransformer model to calculate embeddings of statements in the dataset and the user's input statement. By employing cosine similarity, we identify the top 5 semantically similar petitions from the dataset based on the user's input. This enables us to recommend relevant petitions to the user effectively.
* Transfer Learning using Transformers:
To enhance the performance of our tool, we leveraged transfer learning techniques. We split the dataset into training, validation, and testing sets, employing the google/mt5-small model as our base model. Through fine-tuning and training for 19 epochs, we obtained impressive results.
Experiments & Results:
Through rigorous experimentation, we achieved a ROUGE1 score of 0.49, indicating the successful retrieval of similar petitions using our semantic search methodology.
Key Features:
At the conclusion of this project, we developed a Petition Template Generation Tool with two essential features:
* Petition Description Generation Module: This module generates a descriptive content based on the client's statement, facilitating the preparation of accurate and tailored petitions.
* Recommendation Module: By leveraging our semantic search capabilities, this module retrieves a collection of petitions similar to the client's statement, providing valuable references and insights.
We're thrilled to contribute to the legal field by providing AI based decision support and simplifying the process of text generation in law. Stay tuned for more updates on this exciting project!
linkedin.com/posts/bigdata-lab
Link: lnkd.in/d3D4DGab
linkedin.com/posts/bigdata-lab
Link: lnkd.in/d8-EZY6Z
linkedin.com/posts/bigdata-lab