Update README.md

README.md

@@ -18,10 +18,10 @@ To address these challenges, we explore the potential of Artificial Intelligence
 The main focus of this project is to develop models that can be used for german clinical texts, but the models we used are mainly developed for german context and documents.
 
 ## Objectives
-- Data Extraction: Accurately extracting relevant information from German medical texts, which are predominantly unstructured.
-- Entity Normalization: Standardizing extracted entities to align with recognized medical terminologies.
-- Attribute Identification: Detecting attributes within the medical, such as the position of a diagnosis on the body or the level of truth.
-- Text Summarization: Generating summaries of clinical documents to aid quick comprehension and decision-making.
+- Data Extraction: Accurately extracting relevant information from German medical texts.
+- Entity Normalization: Standardizing extracted entities with medical terminologies.
+- Attribute Identification: Detecting attributes of the entities within the medical texts, such as the position of a diagnosis on the body or the level of truth.
+- Text Summarization: Generating summaries of clinical documents.
 
 ## Data used
 - Annotated Medical Gold-Standard Datasets: Specifically [BRONCO150](https://www2.informatik.hu-berlin.de/~leser/bronco/index.html) and [Cardio:DE](https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY).