Combining Named Entity Recognition Methods for Concept Extraction in Microposts

NER in microposts is a key and challenging task of mining semantics from social media. Our evaluation of a number of popular NE recognizers over a micropost dataset has shown a significant drop-off in results quality. Current state-of-theart NER methods perform much better on formal text than on microposts. However, the experiment provided us with an interesting observation – although individual NER tools did not perform very well on micropost data, we have received recall over 90% when we merged all the results of the examined tools. This means that if we would be able to combine different NE recognizers in a meaningful way, we might be able to get NER in microposts of an acceptable quality. In this paper, we propose a method for NER in microposts, which is designed to combine annotations yielded by existing NER tools in order to produce more precise results than input tools alone. We combine NE recognizers utilizing ML techniques, namely decision tree and random forest using the C4.5 algorithm. The main advantage of the proposed method lies in the possibility of combining arbitrary NER methods and in its application on short, informal texts. The evaluation on a standard dataset shows that the proposed approach outperforms underlying NER methods as well as a baseline recognizer, which is a simple combination of the best underlying recognizers for each target NE class. To the best of our knowledge, up-to-date, the proposed approach achieves the highest F1 score on the #MSM2013 dataset.