Deya Banisakher

School of Computing and Information Sciences


Lecture Information:
  • March 21, 2019
  • 10:00 AM
  • CASE-243

Speaker Bio

Deya Banisakher is a Ph.D. candidate working on Natural Language Processing (NLP) in the Cognac Laboratory (Cognition, Narrative, and Culture) under the supervision of Dr. Mark Finlayson and Dr. Naphtali Rishe. His research interests lie within semantics extraction, document understanding, and discourse analysis. Deya received his B.S. in Computer Engineering and Computer Science from Bethune-Cookman University.

Description

Modeling natural human behavior in understanding written language is crucial for developing true artificial intelligence. For people, words convey certain semantic concepts. While documents represent an abstract concept—they are collections of text organized in some logical structure, that is, sentences, paragraphs, sections, and so on. Like words, these document structures are used to convey a logical flow of semantic concepts. Machines however, only view words as spans of characters, and documents as mere collections of free-text, missing any underlying meanings behind words and the logical structure of those documents.

Automatic semantic concept detection is the process by which the underlying meanings of words are identified and retrieved. My proposed thesis aims at bridging the semantic gap between automatic concept detection and logical document structure understanding. For my doctoral dissertation, I propose to develop a framework for detecting semantic concepts through modeling and integrating the logical document structure—precisely, the document section structure. Thus, given a set of documents, this framework aims at, identifying the unique section structure of those documents, and later, using this structure in detecting implicit semantic concepts behind the documents’ words, sentences, and sections.