首页> 外文会议>3rd workshop on semantic deep learing 2018 >Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
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Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts

机译:票务系统中的重复暹罗LSTM,用于非对称文本的相似性学习和检索

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摘要

The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrieval task, respectively over unsuper-vised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.
机译:我们的工业票务系统的目标是通过与存储在知识库中的历史票证进行匹配来检索输入查询的相关解决方案。查询由主题和描述组成,而历史记录则由主题,描述和解决方案组成。为了检索相关的解决方案,我们使用文本相似性范例来学习查询和历史记录中的相似性。由于查询和不对称长度的票证对中的术语严重不匹配,因此该任务具有挑战性,其中主题是短文本,而描述和解决方案是多句文本。我们提出了一种新颖的暹罗LSTM复制模型,以学习非对称文本对中的相似性,在无监督和有监督的基线上,分别为检索任务提供了22%和7%的增益(Accuracy @ 10)。我们还表明,短文本和长文本的主题和分布式语义特征改善了相似性学习和检索。

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  • 来源
  • 会议地点 Santa Fe(US)
  • 作者单位

    Corporate Technology, Machine-Intelligence (MIC-DE), Siemens AG Munich, Germany,CIS, University of Munich (LMU) Munich, Germany;

    Corporate Technology, Machine-Intelligence (MIC-DE), Siemens AG Munich, Germany;

    CIS, University of Munich (LMU) Munich, Germany;

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  • 正文语种 eng
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