Name: Arabic-ENglish named entities dataset
Description: Arabic-ENglish named entities dataset is created using DBpedia Linked datasets and parallel corpus. For annotating NE in monolingual English corpus we used Gate tool. Our approach is based on linked data entities by mapping them to Gate Gazetteers, and then constructing a type-oriented NE base covering person, Location and organization classes. The second task consists of the use of machine translation to translate these entities and then finally, generating our NE lexicon that encloses the list of Arabic entities that match to the English lists.
Provider: Monastir University - Tunisia, Umm Al-Qura University - Saudi Arabia
Paper Title: Constructing a Lexicon of Arabic-English Named Entity using SMT and Semantic Linked Data
Tasks: machine translation, named entity recognition
Venue Name: The International Arab Journal of Information Technology
Authors: Emna Hkiri, Souheyl Mallat, Mounir Zrigui and Mourad Mars
Abstract: Named Entity Recognition (NER) is the problem of locating and categorizing atomic entities in a given text. In this work, we used DBpedia Linked datasets and combined existing open source tools to generate from a parallel corpus a bilingual lexicon of Named Entities (NE). To annotate NE in the monolingual English corpus, we used linked data entities by mapping them to Gate Gazetteers. In order to translate entities identified by the gate tool from the English corpus, we used moses, a Statistical Machine Translation (SMT) system. The construction of the Arabic-English NE lexicon is based on the results of moses translation. Our method is fully automatic and aims to help Natural Language Processing (NLP) tasks such as, Machine Translation (MT) information retrieval, text mining and question answering. Our lexicon contains 48753 pairs of Arabic-English NE, it is freely available for use by other researchers. 