Context-Aware Lexicon-Based Semantic Similarity of Text

该公司名为Messagepoint，拥有一个基于云的软件平台，为**企业提供服务，使其能够与客户轻松沟通。这种类型的通信的例子包括关于客户帐单信息的简单文本消息和伴随着企业与其客户之间的通信的完整的信件插入。该公司特有的问题是比较不同语言文本信息的含义相似性。基于词典的相似度算法在探索词-关系层次的语义相似空间时，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)的语义相似度时并不是非常有效。自然语言处理方法将整合到该公司的平台中，以生成该公司软件生成的文本消息的浅层语义表示。这种肤浅的语义分析将提供可以比较含义相似性的消息的表示。

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.