Semi-supervised approach based on co-occurrence coefficient for named entity recognition on Twitter

The nature characteristics of data in Social Network Services (SNS) are usually short, contain insufficient information, and often are influenced by noise data, thus popular Named Entity Recognition (NER) methods applied for these data could provide wrong results even if they perform well on well-format documents. Most of NER methods are based on supervised learning techniques which often require a large amount of training dataset to train a good classifier. The Conditional Random Fields (CRF) is an example of supervised learning method, which is a statistical modeling method to predict labels for sequences of input samples. Weak point of these method is only perform well on well-format sentences. However the proper sentences are not used frequently in SNS, such as a lot of tweets on Twitter are combinations of independent terms which are implicitly belonged to a context of a certain discussion topic. In this paper, we propose a method to extract named entities from Social Data using a semi-supervised learning method, it is an extension of CRF method which adapts the new challenge with segmentations of data depending on its context rather considering entire dataset. In experiments, The method is applied on a dataset collected from Twitter, which includes 8,624 tweets for training with 1,915 labeled tweets and 1,690 tweets for testing. Our system product a promised result with the F score of the classification result be approximated to 83.9%.