Sujan Perera's Dissertation Defense: Friday, August 12, 2016
Ph.D. Committee: Drs. Amit Sheth, Advisor; T.K. Prasad, Michael Raymer, and Pablo Mendes (IBM Research)
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. This dissertation focuses on extracting implicit entities and relationships from clinical narratives and extracting implicit entities from Tweets.
This dissertation demonstrates manifestations of implicit constructs in text, studies their characteristics, and develops a solution that is capable of extracting implicit factual information from text. The developed solution starts by acquiring relevant knowledge to solve the implicit information extraction problem. The relevant knowledge includes domain knowledge, contextual knowledge, and linguistic knowledge. The acquired knowledge can take different syntactic forms such as a text snippet, structured knowledge represented in standard knowledge representation languages like Resource Description Framework (RDF) or custom formats. Hence, the acquired knowledge is processed to create models that can be understood by machines. Such models provide the infrastructure to perform implicit information extraction of interest.
This dissertation focuses on three different use cases of implicit information and demonstrates the applicability of the developed solution in these use cases. They are:
- implicit entity linking in clinical narratives,
- implicit entity linking in Twitter,
- implicit relationship extraction from clinical narratives.