Knowledge acquisition in the information age: the interplay between lexicography and natural language processing
[PhD thesis] Knowledge acquisition in the information age: the interplay between lexicography and natural language processing
Author: Luis Espinosa-Anke
Supervisor: Horacio Saggion
Natural Language Processing (NLP) is the branch of Artificial Intelligence aimed at understanding and generating language as close as possible to a human’s. Today, NLP benefits substantially of large amounts of unnanotated corpora with which it derives state-of-the-art resources for text understanding such as vectorial representations or knowledge graphs. In addition, NLP also leverages structured and semi-structured information in the form of ontologies, knowledge bases (KBs), encyclopedias or dictionaries. In this dissertation, we present several improvements in NLP tasks such as Definition and Hypernym Extraction, Hypernym Discovery, Taxonomy Learning or KB construction and completion, and in all of them we take advantage of knowledge repositories of various kinds, showing that these are essential enablers in text understanding. Conversely, we use NLP techniques to create, improve or extend existing repositories, and release them along with the associated code for the use of the community.