Knowledge-driven Implicit Information Extraction

Perera, Sujan. PhD., Department of Computer Science and Engineering, Wright State University, 2016. Knowledge-driven Implicit Information Extraction. Implicit information in unstructured text can be efficiently extracted by bridging syntactic and semantic gaps in natural language usage and by augmenting information extraction techniques with relevant domain and contextual knowledge. Natural language is a powerful tool developed by humans over hundreds of thousands of years. The extensive usage, flexibility of the language, creativity of the human beings, and social, cultural, and economic changes that have taken place in daily life have added new constructs, styles, and features to the language. One such feature of the language is its ability to express ideas, opinions, and facts in an implicit manner. This is a feature that is used extensively in day to day communications in situations such as: 1) expressing sarcasm, 2) when trying to recall forgotten things, 3) when required to convey descriptive information, 4) when emphasizing the features of an entity, and 5) when communicating a common understanding. Consider the tweet ‘New Sandra Bullock astronaut lost in space movie looks absolutely terrifying’ and the text snippet extracted from a clinical narrative ‘He is suffering from nausea and severe headaches. Dolasteron was prescribed’. The tweet has an implicit mention of the entity Gravity and the clinical text snippet has implicit mention of the relationship between medication Dolasteron and clinical condition nausea. Such implicit references of the entities and the relationships are common occurrences in daily communication and they add unique value to conversations. However, extracting implicit constructs has not received enough attention in the information extraction literature. This dissertation focuses on extracting implicit entities and relationships from clinical narratives and extracting implicit entities from Tweets. When people use implicit constructs in their daily communication, they assume the existence of a shared knowledge with the audience about the subject being discussed. This shared knowledge helps to decode implicitly conveyed information. For example, the above Twitter user assumed that his/her audience knows that the actress Sandra Bullock starred in the movie Gravity and it is a movie about space exploration. The clinical professional who wrote the clinical narrative above assumed that the reader knows that Dolasteron is an anti-nausea drug. The audience without such domain knowledge may not have correctly decoded the information conveyed in the above examples.