Loading...
Loading...

Go to the content (press return)

Low-Rank Regularization for High-Dimensional Sparse Conjunctive Feature Spaces in Information Extraction

Author
Primadhanty, A.
Type of activity
Theses
Defense's date
2017-11-17
URL
http://hdl.handle.net/2117/114220 Open in new window
Abstract
One of the challenges in Natural Language Processing (NLP) is the unstructured nature of texts, in which useful information is not easily identifiable. Information Extraction (IE) aims to alleviate it by enabling automatic extraction of structured information from such text sources. The resulting structured information will facilitate easier querying, organizing, and analyzing of data from texts. In this thesis, we are interested in two IE related tasks: (i) named entity classification and (ii) ...
Group of research
GPLN - Natural Language Processing Group
Citation
Primadhanty, A. "Low-rank regularization for high-dimensional sparse conjunctive feature spaces in information extraction". Tesi doctoral, UPC, Departament de Llenguatges i Sistemes Informàtics, 2017.

Participants

Attachments