6 IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
A survey on Bi-Directional Machine translation (MT) approaches of English/ Arabic Languages: Contributions and Limitations
Insaf Ali Simming1, Nasreen Nizamani2, Rubina Shaheen3, Ali Siddiqui4, Agha Kousar5
1,5Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Sindh, Pakistan 2Department of Electronic Engineering Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Sindh, Pakistan 3Shah Abdul Latif University, Khairpur, Sindh, Pakistan 4English Language Development Center (ELDC), Mehran University of Engineering and Technology (MUET), Jamshoro, Sindh, Pakistan
Abstract second language (that is to be translated). The definition by Present work sketches a brief historic perspective of Machine Systran, (2004) have tended in emphasis about machine Translation (MT) application within natural languages. It explores translation that can be stated of not only changing words different types of translation approaches. Therefore, to with the other words but also involves the human comprehend the application of Machine Translation practically, translation that includes system of complicate rules of Arabic and English languages were for study. However, the linguistics. It evolves within semantics, syntax and to a relation of translation between Arabic and English languages is dated back to early its eras of 1950s in USA. The history of small unit of morphology. The definition states “The machine translation has witnessed a continuous flux of successes European Association for Machine Translation” have and fails. After 1980s, the situation of translating English to highlighted: “an application that has work to translate any Arabic languages experienced a remarkable era. Many of the text of natural language into other. It is one of the early achievements have been witnessed through various contributions researches within field of IT Science that not only prove in from different academic disciplines. This paper has briefly stated handling this but now many of the other systems are present about the contributions made in domains of constructive that could make an output. It can be made useful within translation models, machine translation systems and computer many of different fields” European Association for software. It further explores about the true resources of online Machine Translation in 2004. Definition by Doug Arnold translation assistance for natural languages in shape of online and manual dictionaries as a practical representation to a conceptual et al. (1994), has mainly defined to affirm many definitions. framework of translation models that promotes its functionality One of the prominent definition is as “Machine Translation worldwide. The significance of present study lies within a fact that is an attempt to automatically or as a part for the entire it explicitly shows a hierarchal lineage of both languages taken as process to translate a text that is into one human language a sample of study. Secondly, the lingual characteristics of English to the other.” and Arabic languages compared to each other that connect application with some challenges that are raised because of their 1.1.1 View on the Historic Accounts striking contrasts. However, it gives an impetus for future researchers from various disciplines to explore many subtle The authority on Machine Translation, named W. John paradigms within their respective subjects. Hutchins (1995) has significantly researched about various Key words: marked traces about Machine Translation, recorded back to Lingual Characteristics, historic perspectives, Arabic, English, 17th century. Peter Smirnov-Troyanskii of Russia, 1937 MT contributions has enunciated three constructive stages for editing the mechanical machine translations. 1. Introduction 1.1.2 Pinnacle of Age
1.1 Translation in Machines: Definition and There was a great issue on acknowledging the limits to perspectives simple dictionary systems that used to develop approaches for analyzing source texts within stated grammatical It is described as a “process that accomplices software of regulations. In this respect, The Economist (2002) has cited computer in order to convert text of one language to other” in Atlantic Monthly of 1959 by stating as: “For now, Systran (2004). Therefore, it possesses the work into computer systems, or even of the electronic brains are grammar structure of natural language according to set efficient enough towards conceiving load of application regulations along with some assumptions for transferring known to be as machine translation.” However, this structural grammar of First language (source text) into
Manuscript received July 5, 2019 Manuscript revised July 20, 2019
IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019 7 optimism later seemed as “disillusion” within mid-year to Lack of a rule to combine two letters for creating an other Two letters does combine to 1960s. Even though responsible government sponsors have sound. (th sound is reduced create another sound. (th in (in Arabic (Salem et thinking , sh in sheep ( ث utilized MT within US formed broad committee in order to into al.,2008) evaluate and examine about overall field. With this, No “copula verb- to be”. This Automatic Processing Advisory Committee (ALPAC) later results in unique usage of Presence of “copula verb- to be It is of (is, was, etc.) The sky is clear (The way it السماء) (have presented renowned points to 1966 by highlighting subject “I” (GOOGLE The Sky clear = ( صافية Machine translation as less effective, slower and more (Literal) should) There is gender expensive than human translation. Further, the report even There is only differentiation between male and female differentiation between tried to state that there is no benefit of investment within female, male and neutral. within verbs and nouns. Verb form does not field of MT research. Within 1980s, even Hutchins (1995) It lacks neuter gender marks of Japan to have maintained the greatest of all demarcate the genders. It has irregular, flexible word commercial activities for almost different computer agreement and Order. It has a regular word order companies as Japanese into English translation system. The Examples: (VOS), (SVO), arrangement. (OVS) and (VSO) Commonly history of Och’s work (Makin 2003) has stated that it was observed arrangements: The word order is SVO. an improvement within earlier stated achievements onto (VSO) and (SVO) Words usually change their The quality of changing the statistical approaches started back in late 1980s. It shape with respect to their position. They are either in shape of word is present in continued within early phases of 1990s contributed to Arabic language. (Peter F. Brown et al.,) and IBM’s Watson Research Center start, middle or in the end. It has number representation (WRC). The document of Interpretation and Translation of a subject (2004) also accounts to best example of Meteo system and object in terms of It has number representation singular, plural and dual form. of a subject and object in emerged within Montreal of translate to Canadian News It has two numbering systems. terms of either singular or Bulletins to English into French, vice-verse. It was based One that was learnt from plural. Indians and other is the on daily transmission that dates back in 1977. Machine original one. translations have beneficial impact within many fields that An agreement is found as a No any type of agreement is opened new projects within this scenario. (AAAI, 2005) requirement of adjectives to be found between subject and its in with number, case, gender predicative adjective that have reported number of many related innovated and definitiveness with noun. shall be a number or the .gender البيت األحمر contributions. (Red House) and (Red houses) Number: Red house (Sing) (GOOGLE) البيوت الحمراء The sentence is either nominal The sentence is always 2. The Case of Arabic Language for Machine or verbal nominal It possesses three cases It possess nominative, Translation (Nominative, genitive and genitive , accusative and accusative) Dative. (four cases) The form of tenses that 2.1 Comparison of Linguistic Features Within Arabic Form to tenses that represent represent same action in same action in different time and English Languages framework is derived from different time framework can many times represent words one specific root. Example: of different origin. present) Went) (الذهاب) Arabic English Go (past) Example: Go (present form) (ذهب ) West Germanic language of Went (Past form) Semitic Language the Indo Presence and absence of “al- European Origin Taskeel” One of the major Six Set as a first choice of foreign diacritics does alter meaning English language lacks such Languages language(Lingua Franca) of the word. quality ( لطيفة ) = Two forms: Varieties of English (World Example: Nice ( لطيف ) = Modern Standard-Arabic Englishes) (GOOGLE) Gentle (MSA) American English and British It has some words that Dialectic-Arabic (DA) English themselves represent Letters = 28 Letters = 24 ownership attribute with their Vowels = 5 use. s’ after an object usually ( يا ,و ,ألف) Vowels = 3 Aspiration marks: Sukun, Consonants = P, G, V have no Example: represents someone’s belonging. With an attribute (سيارته) = Shadda (Tashdid), Fatha, any existence in Arabic His car kasra, Damma language. (SYSTRAN) usually “of” is employed. :Example (صديق) = Writing style (Direction): Writing style (Direction): Friend of Right to left Left to Right One or two letters are added Ali’s car, Mother of Ahmed. that shows that Word Classes : Word Classes: Verb, Noun, Adjective, Pronoun, Adverb, highlights the possessive Article, Noun, Verb Conjunction, Interjection quality of pronoun. There is a rule of capital word There is no existence of There is presence of both No rule of capitalization order( Lower case- Upper indefinite article. The generic articles. Indefinite and Case) names are represented by “al” definite articles. No Proper trained rules of that functions as definite Indefinite is represented by punctuation Trained rules of punctuation article. a/an. Whereas, definite is by ’the‘ (الرجل) = Example: The man There is no use of silent letters Silent letters are graphically written (BABLYON) Example: An apple. .The president of USA (رجل) = A man 8 IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
It doesnot have the future It focuses on action. 3.6 Hidden-Markov-Model (HMM) (1960s-1970s) tense. Only focuses However, unlike of Arabic, on whether the action was completed or not. (past, present and future). This model was presented by (L. E. Baum et.al). It deals Present tense for base form of Present form of verb is same verb is different with different with Multi-language translations. It functions to compute persons while the smoothing marginal of probabilities for Natural acknowledging different Example: (GOOGLE) persons. language processing tasks based on Statistical approach. , he runs = (هو يركض) she runs Example: He runs, She runs = (أنها تدير) 3.7 Conditional-Random-Fields (CRF) Model: (2001)
3. Models of Machine Translation (MT) This model was presented by (Lafferty et al., 2001). It deals (English into Arabic) and Vice-Versa with English language and in rare cases with other multiple languages. It functions to POS tagging. It is based on 3.1 (ANN) and Grammar Rule Based MT Model Statistical approach. (English into Arabic) Language 3.8 Finite State Transducer-Based (FST) Model: This model was presented by (Marwan Akeel; Ravi Mishra) (2006) (Dept. of IT Engineering, (BHU) India. It includes translating sentences from English into Arabic that are This model was presented by (El Isbihani et al., 2006). It having gerunds, prepositions, infinitives etc. deals with Arabic language and to rare with other multiple languages. It is used for allowing combination of clitics 3.2 Role and Reference Grammar (RRG) Linguistic within language of Arabic in order to avoid ambiguousness Model (1980s) It is based two finite transducers- stripping (prefixes and suffixes). This model was presented by (William A. Foley; Robert Van Valin Jr). It deals with Multi-language translations. It functions on domains that include Logical and 4. Machine Translation Systems communicative functioning, Lexical decomposition, 4.1 ATA System (1959) Analysis of clause structure and Multilingual (Natural language understanding NLU system). It is based on This system was developed in United States of America. It theories towards current functional grammar. translates English language into many other languages including Arabic language and vice-versa. It translates and 3.3 Interlingua Model of Arabic Machine Translation interprets different professional texts and also helps to (MT) foster the professional skill development. It is based on hybrid approach. This model was presented by Salem et.al. It translates Arabic language into English language. It functions to help 4.2 CIMOS System in sorting out the possible combinations for structures. It is based on Transfer model. It translates English language into many other languages including Arabic language and vice-verse. This system 3.4 Target Language Model mainly promotes the online translation; it supports and sponsors various translating software for programming. This model deals to translate multi- languages. It functions to help in describing the well form of a target language. It 4.3 SYSTRAN System (1968) is based on Statistical Machine translation (SMT) model. This system was developed by George University. It 3.5 Support- Vector Machines (SVM) Model: (2004) translates Arabic language into English language. It functions on; Neural machine translation, Hybrid This model was presented by (Diab et al., 2004). It deals translation, XBRL software solution, XML translation with translating Arabic into English language or the other software, Client-server translation, MT solution for US Multi-language translations. It functions in task of intelligence community. It is based on hybrid approach. classification that is used for character based tag set. It is based on supervised technique of learning in separating 4.4 Sakhr System (1982) hyper plane amongst two classes (YAMCHA-Polynomial kernel function). This system was developed by Al Alamiah Electronics Co. LLC (UAE). It translates Arabic language into English IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019 9 language. It is an integrated system. It embeds NLP 4.10 Morph Tagger (Architecture for POS tag to processors, formal grammars, transfer lexicons, and also natural languages) (2008) deals with enterprise- specific terminology. It is on rules- based and statistical-based Approaches. This system developed under (Bar-haim et.al). It translates Arabic or English language into Hebrew languages and 4.5 Babylon System (1997) vice-verse. It functions specific analyzing from morphological analyzers. It is based on SMT (Statistical This system was developed by (Amnon Ovadia (Babylon Machine Translation Model). software Ltd, Israeli public company). It translates English language into Hebrew and various other languages. It 4.11 ProMT Translator (1991) functions on; OCR (Optical Character Recognition), professional translation of almost all official documents, This system was developed by Herzen University (St. mobile application of smartphone and quick grammar Petersburg). It translates English language into various correction with assist to writing, variety of language languages including Arabic and vice-verse. It has functions learning issues for kids as well as adults. It is an online for covering semantic (meaning), morphological (Word interactive machine translation system. level) and syntactic (sentential) regularity of language. RBMT system then generates the given output to make into 4.6 Bing Translator (2007) translated sentences. It is based on Rule-based approach.
This system was developed by Microsoft. It translates 4.12 An Auto System of (SEATS) (2000) English language into Multi languages including Arabic and vice-verse. It functions on translating phrases of the This system was developed by Cairo University, Egypt. It user language. It acquires web page for the purpose of translates English language into Arabic language. It translating based on Statistical based approach. functions by employing subset of rules to English grammar for analyzing the text given within Artificial Intelligence 4.7 GOOGLE Translate (April 2006) (AI). It is based on Transfer approach.
This system was developed by Google. It translates English 4.13 M-Translation System to Translate English Noun language into Multi-languages including Arabic and vice- Phrases to Arabic (2004) verse. It functions on following domains that includes; Browser integration, Mobile applications, API and Google This system was developed by (A., Baraka,H, Shaalan, K., Assistant. It is based on statistical approach. Rafea, and A., Abdel Moniem). It has its functioning in computer science field. It is based on Transfer approach. 4.8 Uni-Arab (2008) 4.14 M-Translator for Middle East (ME) News (1992) This system was developed by (Salem, Hensman and Nolan). It translates Arabic language into English language. This system was developed by (Rafea et al.). It translates It functions in Arabic Sentence tokenizer, Word tokenizer, English language into Arabic language. It functions in Lexicon Data source, Morphology Parser, Syntactic Parser, political news field. It is based on Transfer approach. Syntactic linking (RRG), Logical structure, Semantic to syntax, create English phase. It works under Role and 4.15 (Auto) Translation of Medical Terms and Texts Reference Grammar Linguistic model (RRG) approach. to Arabic (1996)
4.9 MADA (2005) This system was developed by (Pease,C, Boushaba, A.). It translates English language into Arabic language. It has its This was presented by Rambow and Habash. It translates functioning in systemized Nomenclature of Medicine. It is Arabic language into English language and vice-verse. It based on Hybrid structure (Knowledge-based, corpus based functions on following domains that includes; classifying and transfer approach). morphological attributes, selecting the best that matches morphological scheme. It is based on SVMM (Support- 4.16 ECTACO (1990) Vector Machine Model). This system was developed by US based developer to electronic dictionaries, translation software and eBooks. It translates English language into multi-languages including Arabic language and vice-verse. It functions on following domains that includes; E-Book reading, Electronic 10 IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019 translator, scan and translate and online dictionaries. It is 5.4 Skype, (2016) based on transfer approach. This software was developed by Microsoft. It translates 4.17 Mutarijm.Net (2004) English language into multiple languages including Arabic language and vice-verse. It is an online translator. This system was developed by ATA system. It is one of the products of ATA. It mainly deals with Arabic language and 5.5 Parallel Arabic Dialect Corpus (PADIC) (2015) its discourse. It has multiple fields to deal with. This software was developed by (Meftouh et.al). It 4.18 Al-Nakel El-Arabi (1999) translates English language into Modern Standard Arabic (MSA) language. It conducts Experiments to cross dialect This system was developed by CIMOS system. It functions Arabic MT. It is based on Statistical MT experiments from on following domains that includes; dictionaries that has Modern standard Arabic into Dialectal (Arabic). general words for around (150,000 words and phrases), idioms and specialized words regarding (computer, 5.6 Dialectal (Arabic) (DA)-English parallel corpus banking, business, gas and petroleum). It is based on (2012) transfer approach. This software was developed by (Zbib et.al). It translates 4.19 WeinderWCC (1980) Dialectal Arabic (DA) into English language. It translates Non MSA words form Arabic web text into English. It This system was developed in United States of America works on particular Language Model (LM). (USA). It functions on following two domains that include; Personal computers and Mini computers. 5.7 Multi-Mode Morphological Processor (MMMP)
This software was developed by Sakhr software system. It 5. Machine Translation Software of (Arabic is a Morphological analyzer and synthesizer of Arabic into English) or Vice Versa language.
5.1 Golden Al-Wafi Translator (V2.0) (V.4.00) 5.8 Multi-Mode Syntactic Processor (MMSP) (2002) This software was developed by Sakhr software system. It This software was a work of ATA (software Technology), is a used as Parsing of Arabic sentence into its various limited (Saudi Arabia). It possesses following features that constituents. S, V, Ad etc. includes; Scientific terms, text-to-speech capabilities, a spell checker of English language and bilingual interface. 5.9 Arabic Automatic Diacritizer (AAD)
5.2 Al-Mutarjim al-Araby (V.2.0), (V.3.0) (2011) This software was developed by (Mohammad Ahmed, Sayed Ahmed and Ahmed Al Badrashiny). It translates Presented by by (ATA) software Technology limited of English language into Arabic language. It deals with un- Saudi Arabia. It possesses following features that includes; vowelized Arabic texts. It is based on rule-based, statistical English Text-to-Speech, English/Arabic entries, 46 and hybrid methods. specialized science dictionaries, Multi-document translation. It is English into Arabic Dictionary, It works 5.10 Arabic Text Fragmentator (ATF) as English spell checker, The textual Arabic analyzer with or without vowel points in (harakaat), Translation to nouns It works in (auto) mode that divides the text into small that are proper, dispatching the text of Arabic and English fragments. through mail, Import document formats, .doc/.wri/.ps/.rtf, It is based on Transfer model. 5.11 Arabic Automatic Indexer (AAI)
5.3 Maren It identifies key words and phrases. It also helps in creating indices of Arabic books. System was developed by Microsoft. It translates English language into multi- languages including Arabic language 5.12 Arabic Text to Speech (TTS) and vice-verse. It is an online translator. This software was developed by Sakhr software. It translates Arabic language into any natural human IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019 11 sounding synthetic voice. It converts text into man 5.20 CAT Translator Enterprise synthetic voice. It is based on Natural Language processing (NLP) model. This software was developed by Sakhr software system. It is a Computer Aided Translation system (Work Bench 5.13 The Summarizer system). It translates English into Arabic or vice-verse. It works as supporting enterprise for Natural language It summarizes Arabic and English documents. It is based processing technologies. on Automatic Text summarizing schedule.
5.14 Johaina 6. Dictionaries/ dictionary sites of (Arabic into English) and Vice Versa It translates English language into multiple languages including Arabic language. It focuses to translate News 6.1 Al- Waseet Dictionary (1972) broadcasting into Arabic. It is based on Monitoring and Navigation. This dictionary was developed by Arabic Language Academy, Cairo. It translates Arabic into English language. 5.15 IBSAR It deals with multiple fields.
This software is Arabic and English (Text to Speech) 6.2 Lisan Al-Arab Dictionary (1999) translator. It is an integrated bilingual solution for blind self-learning for blind and navigates Web page. It makes a This dictionary was developed by Ibn Manzur Muhammad search of Arabic to English web information, comprehends, ibn Mukarram. It translates Arabic into English language. pen down and dispatch email messages in (MS) outlook, It deals with Quran, Sunnah and poetry. It is having around integrated Arabic speech synthesizer, integrated OCR 7.1., 80,000 entries. reads tables in Microsoft Word and integrated dictionary. 6.3 Al-Qamoos Al-Muheet Dictionary (1994) 5.16 IDRISI This dictionary was developed by Allama Majd al-din This software is Arabic and English General Search Engine Muhammad ibn Yaqub al Fayruzabadi Dar Ihya al Turath translator. It possesses following features that includes; al Arabi, from Beirut. It translates either Arabic into integrating with INSO filters for supporting the MS-office, English language and vice-verse. It deals with Islamic (TXT), (HTML) and (RTF). It helps in complying with entries. Windows NT security schemes. It is bilingual search engine that supports Arabic and English features in it. 6.4 Al-Munjid Dictionary (2002)
5.17 Sakr Categorization Engine This dictionary was developed by Darul-Ishaat. It translates Arabic into Urdu language. It deals with Islamic lexicon This software was developed by Sakhr software system. It entries. is Arabic and English topic organizer. It either translates English into Arabic and vice-verse. It categorizes and 6.5 Al-Wafi School Dictionary (V1.00) Organizes information into tree form or a topic. This dictionary was presented by ATA (Software 5.18 Sakr Corrector Technology), Limited of (Saudi Arabia). It translates either Arabic into English language and vice-verse. It deals with It is an Automatic Corrector. It either translates English into scientific terms. It is based on transfer model. Arabic or vice-verse. It automatically detects then corrects Arabic grammatical and spelling mistakes. 6.6 Al-Wafi Pocket Electronic Translator
5.19 Sakr Enterprise Translation (SET) (MT Solution) This dictionary was under (ATA) – Software Technology Limited in (Saudi Arabia). It translates either Arabic into This software was developed by Sakhr software system. It English language and vice-verse. It deals with scientific is a Bi-directional MT solution. It either translates English terms, text-to-speech capabilities, a spell checker of into Arabic or vice-verse. It translates documents and web English language and bilingual interface. It is based on pages by creating memory databases along with glossaries. transfer model. 12 IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019
6.7 Bi-Directional Trilingual Talking jargons and word alternatives along with the country Arabic/English/French Dictionary (1997) specific uses.
This dictionary was developed by ECTACO. It translates 6.16 Ebn Masr either Arabic into English or French language and vice- verse. It deals with domains of Legal, Medical. It is a This was developed by Ebn Masr. It is a High school talking dictionary. community dictionary that translates Arabic into English language. It deals with Arts and Humanities and languages. 6.8 Sakhr’s Bilingual Dictionary Al-Qamoos 6.17 Dicts Info This dictionary was developed by SAKHR software system. It translates either Arabic into English or vice-verse. It deals It is a general free multi-lingual dictionary project that with Synonym (Arabic and English) Antonym (Arabic and translates Arabic into English language and vice-verse. It is English). a picture dictionary, encyclopedia.
6.9 E.W.LANE Arabic English Lexicon (1983) 6.18 Firdaus (2005)
This dictionary was developed by Edward William Lane This was developed by site of Firdaous.com. It is a (London). It translates Arabic into English language. It phonetics translator that translates Arabic into English deals with roots of Lexicon translation. language and vice-verse. It Translates to phonetics and helps in Online course to learn Arabic. 6.10 Sakhr’s Multilingual Islamic Dictionary 6.19 World Star This dictionary was developed by SAKR. It translates Arabic, French, German or Turkish into English language This was developed by site of Stars21.com. It is a translator or vice-verse. It deals with domains of Islamic scholars and and encyclopedia that translates Arabic into English religion. language and vice-verse.
6.11 World Translator 6.20 Al-Misbar (2000)
It is a general language translator including Arabic and This was developed by site of ATA software. It is an online English languages. It deals with multiple fields. translator that translates Arabic into English language and vice-verse. It deals with multiple fields. 6.12 Sakhr’s Al-Qamoos Multilingual Dictionary 6.21 Al-Muttaqun This was developed by SAKR. It is a general language dictionary that includes Arabic and English languages. It It is an online dictionary that translates Arabic into English deals with multiple fields. language. It deals with Quran and Sunnah.
6.13 Al-Balqa’ 6.22 Foreignword.com (2000-2002)
It is a general language dictionary that translates Arabic into It is an online translator that translates multiple languages English language. It deals with multiple fields. including Arabic and English languages.
6.14 Al-Buraq 6.23 Your Dictionary (1996)
It is a general language dictionary that translates Arabic into It is an online Reference source that translates multiple English language. It deals with multiple fields. languages including Arabic into English language and vice- versa. It deals with Sentences examples, biographies, 6.15 Babylon (1997) synonyms, grammar, quotations, etymology (origin of word) etc. This was developed by Babylon software system. It is an online language dictionary that translates Arabic into 6.24 Arabic Computer Terminology English language. It deals with English Slangs, translation of Arabic to English technical terms along with specialized This was developed by Free Software Arabisation Team. It is an (Arabeyes) the Arabic UNIX project that translates IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.7, July 2019 13 and focuses on Arabic language and English language and References vice-versa. It deals with computer field. [1] AAAI (2005): “Machine Translation” [2] About ECTACO (2004): “About ECTACO) 6.25 Word Reference (2012, 2014, 2015) [3] Al-Salman, Saleh (2004). The Effectiveness of Machine Translation. In IJAIS, International Journal of Arabic- This was developed by Michael Kellogg. It is an online English Studies, 5, 145-160. dictionary (Reference book) that translates English [4] Al-Sughaiyer, I., Al-Kharashi, I.: Arabic morphological language into Arabic language. It deals with multiple fields. analysis techniques: A comprehensive survey. Journal of the American Society for Information Science and Technology 55(3), 189–213 (2004) 6.26 Lawand’s Arabic-English Dictionary [5] Al-Waseet Dictionary (1972). 2nd edition. Istanbul: Islamic Library. It is a general dictionary that translates Arabic into English [6] Al-Wasiti, Salman (2005). al-tafaaCul bayna al-?insaan wa language. It deals with multiple fields. al-?aalah (interaction between humans and machines). Downloaded from
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