15,461 research outputs found
KACST Arabic Text Classification Project: Overview and Preliminary Results
Electronically formatted Arabic free-texts can be found in abundance these days on the World Wide Web, often linked to commercial enterprises and/or government organizations. Vast tracts of knowledge and relations lie hidden within these texts, knowledge that can be exploited once the correct intelligent tools have been identified and applied. For example, text mining may help with text classification and categorization. Text classification aims to automatically assign text to a predefined category based on identifiable linguistic features. Such a process has different useful applications including, but not restricted to, E-Mail spam detection, web pages content filtering, and automatic message routing. In this paper an overview of King Abdulaziz City for Science and Technology (KACST) Arabic Text Classification Project will be illustrated along with some preliminary results. This project will contribute to the better understanding and elaboration of Arabic text classification techniques
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Recently there has been a lot of interest in learning common representations
for multiple views of data. Typically, such common representations are learned
using a parallel corpus between the two views (say, 1M images and their English
captions). In this work, we address a real-world scenario where no direct
parallel data is available between two views of interest (say, and )
but parallel data is available between each of these views and a pivot view
(). We propose a model for learning a common representation for ,
and using only the parallel data available between and
. The proposed model is generic and even works when there are views
of interest and only one pivot view which acts as a bridge between them. There
are two specific downstream applications that we focus on (i) transfer learning
between languages ,,..., using a pivot language and (ii)
cross modal access between images and a language using a pivot language
. Our model achieves state-of-the-art performance in multilingual document
classification on the publicly available multilingual TED corpus and promising
results in multilingual multimodal retrieval on a new dataset created and
released as a part of this work.Comment: Published at NAACL-HLT 201
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
This article introduces a new language-independent approach for creating a
large-scale high-quality test collection of tweets that supports multiple
information retrieval (IR) tasks without running a shared-task campaign. The
adopted approach (demonstrated over Arabic tweets) designs the collection
around significant (i.e., popular) events, which enables the development of
topics that represent frequent information needs of Twitter users for which
rich content exists. That inherently facilitates the support of multiple tasks
that generally revolve around events, namely event detection, ad-hoc search,
timeline generation, and real-time summarization. The key highlights of the
approach include diversifying the judgment pool via interactive search and
multiple manually-crafted queries per topic, collecting high-quality
annotations via crowd-workers for relevancy and in-house annotators for
novelty, filtering out low-agreement topics and inaccessible tweets, and
providing multiple subsets of the collection for better availability. Applying
our methodology on Arabic tweets resulted in EveTAR , the first
freely-available tweet test collection for multiple IR tasks. EveTAR includes a
crawl of 355M Arabic tweets and covers 50 significant events for which about
62K tweets were judged with substantial average inter-annotator agreement
(Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating
existing algorithms in the respective tasks. Results indicate that the new
collection can support reliable ranking of IR systems that is comparable to
similar TREC collections, while providing strong baseline results for future
studies over Arabic tweets
The CoNLL 2007 shared task on dependency parsing
The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results
FROM MARTO TO MARFELINO, A SHIFT IN NAMING IN GOTPUTUK VILLAGE
This is a study of names in a village called Gotputuk. Naming is one of language
manifestation. Therefore, studying the way naming is maintained or shifted can reflect the
language maintenance and shift. Using 1,648 names as data, the study exposes that Javanese
names are still maintained but they are influenced by Arabic names and urban names
One script for two languages. Latin & Arabic in an early allographic papyrus
This contribution presents a unique papyrus letter in Latin script and Latin language and in Latin script and Arabic language that is possible to date, on palaeographic grounds, from the end of the 7th to the 9th century AD. This precious witness is exam- ined under the historical, graphical, linguistic and cultural point of view and its prove- nance is discussed accordingly. An edition of the whole text is provided and a number of correspondences in Arabic are suggested
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