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Knowledge-driven Implicit Information Extraction

Perera, Pathirage Dinindu Sujan Udayanga
http://rave.ohiolink.edu/etdc/view?acc_num=wright1472474558

Abstract Details

2016, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
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 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. This dissertation demonstrates manifestations of implicit constructs in text, studies their characteristics, and develops a software solution that is capable of extracting implicit 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 such as the Resource Description Framework (RDF) or other custom formats. Hence, the acquired knowledge is pre-processed to create models that can be processed by machines. Such models provide the infrastructure to perform implicit information extraction. 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: 1) implicit entity linking in clinical narratives, 2) implicit entity linking in Twitter, and 3) implicit relationship extraction from clinical narratives. The evaluations are conducted on relevant annotated datasets for implicit information and they demonstrate the effectiveness of the developed solution in extracting implicit information from text.
Amit Sheth, Ph.D. (Advisor)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Michael Raymer, Ph.D. (Committee Member)
Pablo Mendes, Ph.D. (Committee Member)
123 p.
Computer Science
implicit entities; implicit relationships; information extraction; knowledge base

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Citations

  • Perera, P. D. S. U. (2016). Knowledge-driven Implicit Information Extraction [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472474558

    APA Style (7th edition)

  • Perera, Pathirage. Knowledge-driven Implicit Information Extraction. 2016. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1472474558.

    MLA Style (8th edition)

  • Perera, Pathirage. "Knowledge-driven Implicit Information Extraction." Doctoral dissertation, Wright State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472474558

    Chicago Manual of Style (17th edition)

wright1472474558
1,260
© 2016, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.