Extraction of Key Words from News Stories

In this work, we consider the task of extracting key-words such as key-players, key-locations, key-nouns and key-verbs from news stories. We cast this problem as a classification problem wherein we assign appropriate labels to each word in a news story. We considered statistical models such as naïve Bayes model, hidden Markov model and maximum entropy model in our work. We have also experimented with various features. Our results indicate that a maximum entropy model that ignores contextual features and considers only word-based features combined with stopping and stemming yields the best performance. We found that extraction of keyverbs and key-nouns is a much harder problem than extracting keyplayers and key-locations.