Identification and characterization of diseases on social web

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Deep Learning based Pipeline with Multichannel Inputs for Patent Classification

Patent document classification as groundwork has been a challenging task with no satisfactory performance for decades. In this work, we introduce a deep learning pipeline for automatic patent classification with multichannel inputs based on LSTM and word vector embeddings. Sophisticated text mining methods are used to extract the most important segments from patent texts, and a domain-specific pre-trained word embeddings model for the patent domain is developed; it was trained on a very large dataset of more than five million patents. A deep neural network model is trained with multichannel inputs namely embeddings of different segments of patent texts, and sparse linear input of different metadata. A series of patent classification experiments are conducted on different patent datasets, and the experimental results indicate that using the segments of patent texts as well as the metadata as multichannel inputs for a deep neuralnetwork model, achieves better performance than one input channel.

Deep Learning Services for Patents

Most of word embedding techniques provide only one vector representation for each word in a text corpus, despite the fact that a single word could have multiple meanings. In this paper, we developed a domain-specific word and phrase embedding model for the patent domain. It treats patent phrases as single information units. Natural language processing techniques are used to extract the meaningful terms from five million patent documents, and a word embedding algorithm is used for generating semantic representation of those terms. This model can be used for a wide rage of tasks like search query expansion, patent semantic similarity search, enrichment and for supporting other patent text mining tasks like patent technology categorization, and knowledge discovery.