Incorporating Syntactic Dependencies into Semantic Word Vector Model for Medical Text Processing

摘要

Natural language processing has been split into two sub-fields: semantic and syntactic analysis. Semantic processing deals with creating vector representations for each word. The goal is for the vectors to be dense and that relationships between words are made clear by their vectors. Word2Vec Skip-Gram model is one such semantic word embedding model that processes the words into vectors based on the co-occurrences of the words within a fixed context window. Syntactic processing, on the other hand, deals with gleaning textual information from the structure of the text. A popular method of syntactic processing is dependency parsing, where connections are explicitly drawn between different pairs of words in a phrase or clause, where one word modifies the other. Human beings make use of both semantic and syntactic information when processing text. As such, combining semantic and syntactic processing is important for medical text analysis. The dependencies between words define specific medical concepts, such as diseases and symptoms. The aim of this research paper is to investigate an altered word vector model-the SD Skip-Gram model which incorporates the dependencies between words. The SD Skip-Gram model is compared against the basic Skip-Gram model for analyzing common words and medical disease and symptom concepts. The returned results demonstrate that the SD Skip-Gram model can identify the relationships between the common words as well as the basic Skip-Gram model and perform better than the basic Skip-Gram model in identifying the related disease and symptom concepts.