Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 size_categories:
 - 100K<n<1M
 ---
-# Dataset Building
+# Greek NER dataset
 
 ## Acknowledgement
 
@@ -20,6 +20,8 @@ This dataset had been created as part of joint research of HUMADEX research grou
 Authors:
 dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc
 
+## Dataset Building
+
 ## Data Integration and Preprocessing
 
 We begin by merging two distinct datasets of English medical texts. This step ensures a robust and diverse corpus, combining the strengths of both datasets. Following the integration, we preprocess the texts to clean the data, which includes removal of strings that do not contain relevant information. This preprocessing step is crucial to ensure the texts are in an optimal format for subsequent annotation.
@@ -56,7 +58,8 @@ The data underwent a preprocessing process.
 The preprocessed English texts are then annotated using [Stanza's i2b2 Clinical Model](https://stanfordnlp.github.io/stanza/available_biomed_models.html). This model is specifically designed for clinical text processing, and it annotates each text with three labels:
    - **PROBLEM**: Includes diseases, symptoms, and medical conditions.
    - **TEST**: Represents diagnostic procedures and laboratory tests.
-   - **TREATMENT**: Covers medications, therapies, and other medical interventions.
+   -
+ **TREATMENT**: Covers medications, therapies, and other medical interventions.
 
 This annotation step is essential for creating a labeled dataset that serves as the foundation for training and evaluating Named Entity Recognition (NER) models.