In this research, I try to solve multi-class multi-label classication problem, where

the goal is to automatically assign one or more labels(tags) to discussion topics seen

in deepweb. I observed natural hierarchy in our dataset, and I used dierent

techniques to ensure hierarchical integrity constraint on the predicted tag list. To

solve `class imbalance' and `scarcity of labeled data' problems, I developed semisupervised

model based on elastic search(ES) document relevance score. I evaluate

our models using standard K-fold cross-validation method. Ensuring hierarchical

integrity constraints improved F1 score by 11.9% over standard supervised learning,

while our ES based semi-supervised learning model out-performed other models in

terms of precision(78.4%) score while maintaining comparable recall(21%) score.