The temporal relation or temporal links (denoted by the TLink tag) in Chinese electronic medical records includes temporal relations within a sentence (hereafter referred to as "within-sentence TLinks"), and between-sentence TLinks. Among them, within-sentence TLinks include event/event TLinks and event/time TLinks, and between-sentence TLinks include event/event TLinks. The recognition of temporal relation in Chinese electronic medical record was transformed into classification problem on entity pairs. Heuristic rules with high accuracy were developed and two different classifiers with basic features, phrase syntax, dependency features, and other features were trained to determine within-sentence TLinks. Apart from heuristic rules with high accuracy, basic features, phrase syntax, and other features were used to train the classifiers to determine between-sentence TLinks. The experimental results show that Support Vector Machine (SVM), SVM and Random Forest (RF) algorithms achieve the best performance of recognition on within-sentence event/event TLinks, within-sentence event/time TLinks and between-sentence event/event TLinks, with F ₁-scores of 84.0%, 85.6% and 63.5% respectively.

To help users to quickly retrieve the interesting game strategies, a knowledge driven automatic annotating algorithm for game strategies was proposed. In the proposed algorithm, the game domain knowledge base was built automatically by fusing multiple sites that provide information for each game. By using the game domain vocabulary discovering algorithm and decision tree classification model, game terms of the game strategies were extracted. Since most terms existing in the strategies in the form of abbreviation, the game terms were finally linked to knowledge base to generate the full name semantic tags for them. The experimental results on many games show that the precision of the proposed game strategy annotating method is as high as 90%. Moreover, the game domain vocabulary discovering algorithm has a better result compared with the n-gram language model.