面向业务数据分析的高级深度融合
Character-level Deep Conflation for Business Data Analytics
摘要：
连接与同一实体关联的不同文本属性（合并）在业务数据分析中很重要，因为它可以帮助合并数据库中的两个不同表，以提供更全面的实体配置文件。然而，合并任务是具有挑战性的，因为描述同一实体的两个文本字符串可能由于拼写错误等原因而彼此完全不同。因此，开发一个能够真正理解字符串的语义并在语义级别匹配它们的合并模型至关重要。为此，我们开发了一个字符级深度合并模型，该模型将输入文本串从字符级编码为有限维特征向量，然后用于计算文本串之间的余弦相似度。使用反向传播和随机梯度下降以端到端的方式训练模型，以最大化正确关联的可能性。具体来说，我们提出了深度合并模型的两种变体，分别基于长短期记忆（LSTM）递归神经网络（RNN）和卷积神经网络（CNN）。这两种模型在真实世界的业务分析数据集上表现良好，并且显著优于基线特征袋（BoC）模型。
英文摘要：
Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity. However, the conflation task is challenging because two text strings that describe the same entity could be quite different from each other for reasons such as misspelling. It is therefore critical to develop a conflation model that is able to truly understand the semantic meaning of the strings and match them at the semantic level. To this end, we develop a character-level deep conflation model that encodes the input text strings from character level into finite dimension feature vectors, which are then used to compute the cosine similarity between the text strings. The model is trained in an end-to-end manner using back propagation and stochastic gradient descent to maximize the likelihood of the correct association. Specifically, we propose two variants of the deep conflation model, based on long-short-term memory (LSTM) recurrent neural network (RNN) and convolutional neural network (CNN), respectively. Both models perform well on a real-world business analytics dataset and significantly outperform the baseline bag-of-character (BoC) model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
