Automatic Annotation of Locative and Directional Expressions in Arabic

05/16/2018
by   Rita Hijazi, et al.
0

In this paper, we introduce a rule-based approach to annotate Locative and Directional Expressions in Arabic natural language text. The annotation is based on a semantic map of the spatiality domain. Challenges are twofold: first, we need to study how locative and directional expressions are expressed linguistically in these texts; and second, we need to automatically annotate the relevant textual segments accordingly. We will validate this method on specific novel rich with these expressions and show that it has very promising results. We will be using NOOJ as a software tool to implement finite-state transducers to annotate linguistic elements according to three semantic classes: projective, topological and directional.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/29/2018

Automatic Identification of Arabic expressions related to future events in Lebanon's economy

In this paper, we propose a method to automatically identify future even...
research
05/24/2016

Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign Language MT

In this paper, we present an ongoing effort in lexical semantic analysis...
research
03/17/2022

Towards Responsible Natural Language Annotation for the Varieties of Arabic

When building NLP models, there is a tendency to aim for broader coverag...
research
09/17/2017

MERF: Morphology-based Entity and Relational Entity Extraction Framework for Arabic

Rule-based techniques and tools to extract entities and relational entit...
research
08/15/2018

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

Data annotation is an important but time-consuming and costly procedure....
research
05/29/2020

A frame semantics based approach to comparative study of digitized corpus

in this paper, we present a corpus linguistics based approach applied to...
research
12/01/2016

Multilingual Multiword Expressions

The project aims to provide a semi-supervised approach to identify Multi...

Please sign up or login with your details

Forgot password? Click here to reset