Comparison and Analysis of Embedding Methods for Patent Documents

Patent text mining is an important task that requires domain knowledge. The patent text is sometimes not clear and contains many ambiguous and technical words. Traditional text mining approaches are not satisfactory enough for patent text mining. In this paper, we consider various embedding techniques for patent documents and try to find how to represent the patent text for other downstream tasks such as patent classification, patent recommendation, finding similar patents, knowledge mining, etc. We compare several embedding approaches with the patent classification task. The experimental results demonstrate that using contextual word embeddings can perform better than the conventional static word embedding approaches.