Adapting the JIRS Passage Retrieval System to the Arabic Language

Benajiba, Yassine; Rosso, Paolo; Soriano, José Manuel Gómez

doi:10.1007/978-3-540-70939-8_47

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…3) Miscellaneous Corpora, such as Speech Corpora [46], Handwriting Recognition Corpora [47], are beneficial for a number of NLP correlated tasks such as plagiarism detection [48], speech recognition systems [46] and question answering [49]. 4) Annotated corpora are essential for the development of many NLP systems, such as part-of-speech tagging [24], text parsing [50].…”

Section: Web-based Corpora Such As the Kacst Arabic Corpusmentioning

confidence: 99%

BAAC: Bangor Arabic Annotated Corpus

Alkhazi¹,

William²

2018

ijacsa

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This paper describes the creation of the new Bangor Arabic Annotated Corpus (BAAC) which is a Modern Standard Arabic (MSA) corpus that comprises 50K words manually annotated by parts-of-speech. For evaluating the quality of the corpus, the Kappa coefficient and a direct percent agreement for each tag were calculated for the new corpus and a Kappa value of 0.956 was obtained, with an average observed agreement of 94.25%. The corpus was used to evaluate the widely used Madamira Arabic part-of-speech tagger and to further investigate compression models for text compressed using partof-speech tags. Also, a new annotation tool was developed and employed for the annotation process of BAAC. Keywords-Component; arabic language; corpus; annotated corpora; analysis results I. BACKGROUND AND MOTIVATION The Arabic language ‫"انعربيت"‬ is acknowledged to be one of the most largely used languages, with 330 million people using the language as their first language, as shown in Table 1, plus 1.4 billion more using it as a secondary language [1]. The majority of the speakers are located across twenty-two nations, primarily in the Middle East, North Africa and Asia, and the United Nations considers the Arabic language as one of its five official languages. The Arabic language is part of the Semitic languages that includes Tigrinya, Amharic, Hebrew, etc., and shares almost the same structure as those languages. It has 28 letters, two gendersfeminine and masculine, as well as singular, dual and plural forms. The Arabic language has a right-to-left writing system with the basic grammatical structure that consists of verb-subject-object and other structures, such as VOS, VO and SVO [2]-[4]. TABLE I. THE MOST UNIVERSALLY USED LANGUAGES Rank Language Users (millions)

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Section: Web-based Corpora Such As the Kacst Arabic Corpusmentioning

confidence: 99%

BAAC: Bangor Arabic Annotated Corpus

Alkhazi¹,

William²

2018

ijacsa

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“…Most of the QA systems are composed of three subtasks: question analysis, passage or document retrieval and answer extraction. Different QA systems may use different implementation for each task (Benajiba, Rosso and Soriano 2007; Ezzeldin and Shaheen 2012).…”

Section: Related Workmentioning

confidence: 99%

Lemaza: An Arabic why-question answering system

Azmi¹,

AlShenaifi²

2017

Nat. Lang. Eng.

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Question answering systems retrieve information from documents in response to queries. Most of the questions arewho-andwhat-type questions that deal with named entities. A less common and more challenging question to deal with is thewhy-question. In this paper, we introduceLemaza(Arabic forwhy), a system for automatically answeringwhy-questions for Arabic texts. The system is composed of four main components that make use of the Rhetorical Structure Theory. To evaluateLemaza, we prepared a set ofwhy-question–answer pairs whose answer can be found in a corpus that we compiled out of Open Source Arabic Corpora.Lemazaperformed best when the stop-words were not removed. The performance measure was 72.7%, 79.2% and 78.7% for recall, precision andc@1, respectively.

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“…x A knowledge based Arabic Question Answering System (AQAS) [6] the main concept of this system that it uses a knowledge based question answering system and it does not use raw data it uses structured data instead. x Arabic question answering System (ArabiQA) [7], [8] is an Arabic Q/A system based on passage retrieval system module and Named Entity Recognition based on maximum entropy approach which was developed by themselves [9]. They have reached precision to 83.3%.…”

Section: Backgroungmentioning

confidence: 99%

Enhancing Arabic Question Answering System

Kamal¹,

Azim²

2014

2014 International Conference on Computational Intelligence and Communication Networks

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During the last few decades many Arabic question answering systems have been developed. These systems may extract answers from texts or web-pages. None of these systems make use of question answering database where user can present questions in natural language which differ from the stored questions. The proposed system uses information retrieval approaches to get to the closest answers to the input question, so the system gives partially or totally correct answers. The Latent Semantic Indexing (LSI) is implemented to enhance the resultant selections. Arabic natural language processing is used in the proposed system along with LSI.Keywords-question answering system; natural language processing; information retrieval; latent semantic indexing I.

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Adapting the JIRS Passage Retrieval System to the Arabic Language

Cited by 16 publications

References 21 publications

BAAC: Bangor Arabic Annotated Corpus

BAAC: Bangor Arabic Annotated Corpus

Lemaza: An Arabic why-question answering system

Enhancing Arabic Question Answering System

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