Word Embedding Based Document Similarity for the Inferring of Penalty

@inproceedings{He2018WordEB,
  title={Word Embedding Based Document Similarity for the Inferring of Penalty},
  author={Tieke He and Hao Lian and Zemin Qin and Zhipeng Zou and Bin Luo},
  booktitle={WISA},
  year={2018}
}
In this paper, we present a novel framework for the inferring of fine amount of judicial cases, which is based on word embedding when calculating the distances between documents. Our work is based on recent studies in word embeddings that learn semantically meaningful representations for words from local occurrences in sentences. This framework considers the context information of words by adopting the word2vec embedding, compared to traditional processing methods such as hierarchical… CONTINUE READING

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