Update README.md

README.md

@@ -13,4 +13,77 @@ tags:
 pretty_name: crime
 size_categories:
 - 1K<n<10K
----
+---
+About Dataset
+Bengali Crime Event Dataset – Law & Order, Governance & Social Issues (2025)
+
+This dataset contains Bengali news headlines annotated for crime and event understanding across law & order, legal activity, governance, and social-issue contexts.
+
+Each record represents a real-world headline paired with four aligned labels capturing:
+  Event type
+  Location
+  Participants
+  Action / outcome
+  
+The dataset is designed for NLP research, media analytics, and policy-oriented analysis in low-resource languages.
+
+Note: The dataset reflects news reporting, not legal judgments or verified outcomes.
+
+Data Source & Curation:
+  a. Headlines are collected from publicly available Bengali news sources
+  b. All labels are manually standardized into consistent label spaces
+  c. Headlines: Bengali
+  d. Labels: English (for broader usability and cross-lingual research)
+
+Dataset Structure:
+  Total records: 1K–10K headlines
+  Granularity: One headline = one primary event
+
+Fields per record:
+  Field	Description
+  Title	Bengali news headline (UTF-8)
+  EventType	High-level category (Violence / Legal Activity / etc.)
+  Location	City or region tag
+  Participants, Actor, group
+  Action, Procedural state or outcome
+
+Key Features:
+  1. Multi-head labels: Enables joint or hierarchical modeling
+  2. Low-resource friendly: Bengali text with English labels
+  3. Extendable geography: Can be linked with gazetteers
+
+Potential Use Cases:
+  a. Text Classification: Single- or multi-head prediction
+  b. Information Extraction: Actor/action inference from headlines
+  c. Trend Analysis: City-wise or actor-wise event patterns
+  d. Policy & Media Studies: Aggregate reporting insights
+
+Recommended Splits & Metrics:
+  Split: Stratified 70/15/15 or 70/10/20
+
+Metrics:
+  1. Macro-F1 (primary)
+  2. Micro-F1 / Accuracy (secondary)
+
+Imbalance handling:
+  1. Class weights
+  2. Focal loss
+  3. Balanced sampling
+
+License:
+  This dataset is released under CC BY 4.0.
+  Free to use, share, and adapt for research and educational purposes.
+  Please use responsibly and avoid individual-level attribution or predictive policing.
+
+Citation:
+  @inproceedings{hossain2025masknet,
+    title={MaskNet: Enhancing Crime Event Detection with Feature Masking and Dynamic Attention},
+    author={Hossain, M. M. and Hossain, M. S. and Chaki, S. and Rahman, M. S. and Ali, A. S.},
+    booktitle={Proceedings of the 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM)},
+    year={2025},
+    publisher={IEEE}
+  }
+
+GitHub Repository:
+  https://github.com/MIthun667/Crime-Event-Detections/tree/main
+