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Article summary:
1. This paper proposes TransR, a model for building entity and relation embeddings in separate entity space and relation spaces.
2. Experiments are conducted to evaluate the performance of TransR on three tasks: link prediction, triple classification and relational fact extraction.
3. Results show that TransR outperforms state-of-the-art baselines including TransE and TransH in all three tasks.
Article analysis:
The article is generally trustworthy and reliable as it provides detailed information about the proposed model, its evaluation on three tasks, and comparison with existing models. The authors have provided evidence for their claims by conducting experiments and providing results which demonstrate the superiority of their proposed model over existing models. The article does not appear to be biased or one-sided as it presents both sides of the argument equally. It also does not contain any promotional content or partiality towards any particular model or approach. The authors have noted possible risks associated with their proposed model such as overfitting due to limited training data, which is commendable. All in all, the article appears to be well researched and presented without any major flaws or biases.