ArabRelat: Arabic Relation Extraction using Distant Supervision
Year: 2015
Type: conference-paper
Abstract: Relation Extraction is an important preprocessing task for a number of text mining applications, including:
Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a
novel Arabic relation extraction method that leverages linguistic features of the Arabic language in Web data
to infer relations between entities. Due to the lack of labeled Arabic corpora, we adopt the idea of distant
supervision, where DBpedia, a large database of semantic relations extracted from Wikipedia, is used along
with a large unlabeled text corpus to build the training data. We extract the sentences from the unlabeled text
corpus, and tag them using the corresponding DBpedia relations. Finally, we build a relation classifier using
this data which predicts the relation type of new instances. Our experimental results show that the system
reaches 70% for the F-measure in detecting relations.
Authors: Reham Mohamed, Nagwa M. El-Makky, Khaled Nagi
Institution: Alexandria University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2295301034