Abstract
Along with the explosion of web information, information flow service has attracted the attentions of users. In this kind of service, how to measure the similarity between texts and further filter the redundant information collected from multiple sources becomes the key solution to meet user’s desire. One text often mentions several events. The core event mostly decides the main content carried by the text. It should take the pivotal position. For this reason, this paper aims to construct a passage-level event connection graph to model the relations among the events mentioned by one text. The core event can be revealed and is further chosen to measure the similarity between two texts. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our unsupervised measuring method acquires superior results than unsupervised methods by a large margin and even comparable results with some popular supervised neuron based methods.
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Acknowledgement
The research in this article is supported by the Science and Technology Innovation 2030 - “New Generation Artificial Intelligence” Major Project (2018AA0101901), the National Key Research and Development Project (2018YFB1005103), the Key Project of National Science Foundation of China (61632011), the National Science Foundation of China (61772156, 61976073) and the Foundation of Heilongjiang Province (F2018013).
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Liu, M., Zheng, Z., Qin, B., Liu, Y. (2020). A Passage-Level Text Similarity Calculation. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_17
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DOI: https://doi.org/10.1007/978-3-030-60450-9_17
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