Context-Aware Lexicon-Based Semantic Similarity of Text

这家名为Messagepoint的公司拥有一个基于云的软件平台，为企业提供服务，使他们能够轻松地与客户沟通。此类通信的示例包括关于客户账单信息的简单文本消息，以及伴随企业与其客户之间通信的完整的信件插入。该公司特有的问题是比较多种语言的文本消息的含义相似性。基于词典的相似度算法在沿着词关系层次探索语义相似空间时，在词匹配的上下文中表现得很好。然而，这些以单词为中心的算法在计算更大的单词集（如句子或段落）的语义相似度时不是很有效。自然语言处理方法将被集成到公司的平台中，以便生成公司软件生成的文本消息的浅层语义表示。这种浅层语义分析将提供可以比较意义相似性的消息表示。

The company, Messagepoint, has a cloud-based software platform that provides services for**businesses to enable easy communication with their customers. Examples of this type of communication include simple text messages on customer billing information and complete letter inserts that accompany communication between a business and its customers. The company-specific problem is the comparison of text messages in multiple languages for meaning similarity. Lexicon-based similarity algorithms have shown to work well in the context of word matching when exploring the semantic similarity space along word-relation hierarchies. However, these word-focused algorithms, are not very effective when computing semantic similarity for a larger set of words (such as sentences or paragraphs). Natural Language Processing methods will be integrated into the company's platform in order to generate a shallow semantic representation of the text messages generated by the company's software. This shallow semantic analysis will provide representations of the messages that can be compared for similarity of meaning.