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      An Efficient Approach to Learning Chinese Judgment Document Similarity Based on Knowledge Summarization

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          Abstract

          A previous similar case in common law systems can be used as a reference with respect to the current case such that identical situations can be treated similarly in every case. However, current approaches for judgment document similarity computation failed to capture the core semantics of judgment documents and therefore suffer from lower accuracy and higher computation complexity. In this paper, a knowledge block summarization based machine learning approach is proposed to compute the semantic similarity of Chinese judgment documents. By utilizing domain ontologies for judgment documents, the core semantics of Chinese judgment documents is summarized based on knowledge blocks. Then the WMD algorithm is used to calculate the similarity between knowledge blocks. At last, the related experiments were made to illustrate that our approach is very effective and efficient in achieving higher accuracy and faster computation speed in comparison with the traditional approaches.

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          Term-weighting approaches in automatic text retrieval

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            Short Text Similarity with Word Embeddings

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              Retrieval, reuse, revision and retention in case-based reasoning

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                Author and article information

                Journal
                06 August 2018
                Article
                1808.01843
                7d416c9d-59ae-4ef3-b8bf-ded3363a932a

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                68T35
                23 pages
                cs.AI cs.CL cs.IR

                Theoretical computer science,Information & Library science,Artificial intelligence

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