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  • Title: QAMUS: Arabic Lexicography (Tim Buckwalter's website)
    Descriptive info: .. QAMUS: Arabic Lexicography.. CORPUS COMPILATION.. WORD FREQUENCY COUNTS.. CONCORDANCING.. MORPHOLOGY ANALYSIS.. ARABIC LEXICON.. TimBuckwalter@qamus.. org.. Copyright © 2002-2003 QAMUS LLC..

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  • Title: Arabic Corpus Compilation
    Descriptive info: ARABIC CORPUS COMPILATION.. The first task in Arabic lexicography is corpus compilation.. Arabic corpora can be acquired from the.. Linguistic Data Consortium.. (LDC) and the.. European Language Resources Association.. (ELRA).. Current popular corpora consist mainly of AFP newswire and the archives of the daily paper Al-Hayat.. Corpora can also be obtained by contacting the original publishers themselves:.. Al-Hayat.. , for example, has been known to publish an annual CD-ROM of archived texts.. Corpora can also be obtained by data-mining websites directly.. The.. Google Directory.. lists hundreds of Arabic-language websites.. Most websites store  ...   store Arabic in PDF files, from which Arabic text cannot be easily reconstituted.. The following is a partial listing of Arabic newspapers with sizable archives on the Web:.. Al-Ahram.. (Cairo).. http://www.. ahram.. eg.. Al-Bayan.. (UAE).. albayan.. co.. ae.. Al-Dustur.. (Amman).. addustour.. com.. (London).. alhayat.. Al-Nahar.. (Beirut).. annaharonline.. Al-Raya.. (Qatar).. raya.. Al-Riyadh.. (Riyadh).. alriyadh-np.. Al-Safir.. assafir.. Al-Sharq Al-Awsat.. asharqalawsat.. Al-Watan.. al-watan.. Once a corpus is compiled, the next task is to assess its size in terms of.. types.. and.. tokens.. (see.. ).. HOME.. | CORPUS COMPILATION |.. |.. Copyright © 2002 QAMUS LLC..

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  • Title: Arabic Word Frequency Counts
    Descriptive info: ARABIC WORD FREQUENCY COUNTS.. When tokenizing a text for purposes of generating a word frequency count, I define an Arabic word as:.. one or more consecutive Arabic characters.. [\xC1-\xD6\xD8-\xDB\xDD-\xDF\xE1\xE3-\xE6\xEC\xED].. including Persian characters.. [\x81\x8D\x8E\x90].. short vowels and diacritics.. [\xF0-\xF3\xF5\xF6\xF8\xFA].. and the lengthening character.. [\xDC].. Note: All hex values are those of the.. Arabic Windows (1256).. code page.. When tokenizing Arabic input it's a good idea to make a preliminary pass to detect and fix punctuation anomalies, such as the Arabic character.. ra'.. (.. \xD1.. ) used as a numeric comma or decimal separator (.. U+066B.. ), and the Arabic lengthening character (.. \xDC.. ) used as an em dash or numeric hyphen.. Numbers are sometimes encoded visually instead of logically, and the digit zero occasionally functions as a period (full stop).. Aften tokenizing according to the above criteria I remove all short vowels, diacritics and the lengthening character, and count the remainder as a word.. Null strings are discarded.. Here are the types/tokens figures from the last three word frequencies I have generated:.. date.. Feb.. 1999.. 1,359,309.. 167,216,930.. 2001.. 2,578,709.. 589,184,483.. Aug.. 2002.. 3,509,499.. 1,141,563,654.. The table below shows the top 30 words and their frequencies from my three frequency counts.. Note that starting with my Feb.. 2001 count I added a file count figure and began to use it instead of the frequency count as the primary sort key.. On Nov.. 13, 2002 I wrote a script to get the Google frequency: it's an interesting statistic, especially if you look at the web as the Mother of All Corpora.. Nov.. rank.. word.. frequency.. file count.. Google.. 1.. في.. 5,645,218.. من.. 13,624,732.. 1,144,319.. 26,533,543.. 2,511,236.. 6,600,000.. 2.. 3,871,153.. 18,817,693.. 1,128,546.. 36,615,810.. 2,422,564.. 7,540,000.. 3.. على.. 2,310,879.. 7,508,546.. 915,406.. 14,173,880.. 1,996,755.. 4,320,000.. 4.. ان.. 2,219,600.. عن.. 3,669,877.. 776,762.. 6,717,942.. 1,684,706.. 2,660,000.. 5.. الى.. 1,516,247.. 6,518,011.. 748,416.. 12,214,896.. 1,637,296.. 1,450,000.. 6.. التي.. 1,072,702.. 3,530,111.. 733,062.. 6,963,708.. 1,625,725.. 1,710,000.. 7.. 933,872.. 4,228,886.. 666,316.. 7,861,905.. 1,424,032.. 1,200,000.. 8.. الذي.. 727,170.. 2,441,785.. 645,854.. 4,712,703.. 1,406,014.. 1,370,000.. 9.. مع.. 673,928.. 2,307,276.. 618,865.. 4,597,178.. 1,401,634.. 1,500,000.. 10.. هذا.. 664,751.. 2,262,268.. 562,428.. 4,234,060.. 1,216,084.. 1,730,000.. 11.. هذه.. 621,972.. 2,056,768.. 533,077.. 3,936,525.. 1,167,126.. 1,440,000.. 12.. ما.. 614,348.. 2,134,871.. 516,655.. بعد.. 2,728,220.. 1,119,473.. 1,240,000.. 13.. لا.. 596,737.. بين.. 1,557,175.. 498,405.. 3,016,512.. 1,103,814.. 1,230,000.. 14.. 471,859.. 1,355,002.. 498,352.. 3,981,028.. 1,102,480.. 2,100,000.. 15.. أن.. 444,508.. 2,053,785.. 448,718.. خلال.. 2,285,299.. 983,953.. 624,000.. 16.. ذلك.. 390,446.. 1,128,034.. 433,457.. كما.. 2,236,668.. 952,957.. 947,000.. 17.. كان.. 385,909.. 1,148,968.. 429,963.. 3,780,512.. 949,315.. 2,000,000.. 18.. 383,454.. كل.. 1,264,232.. 415,445.. 4,960,500.. 944,445.. 2,320,000.. 19.. 372,917.. 1,336,611.. 414,018.. 2,373,084.. 876,683.. 1,100,000.. 20.. لم.. 347,762.. 2,222,026.. 395,224.. 2,432,523.. 874,247.. 1,190,000.. 21.. إلى.. 336,817.. 1,316,091.. 392,905.. 2,417,022.. 828,361.. 1,170,000.. 22.. بن.. 330,130.. 1,165,657.. 378,694.. 2,211,351.. 817,968.. 1,060,000.. 23.. 316,837..  ...   103.. 80.. والاستقلاب.. 338,618.. 46.. 43.. استقلابية.. 402,948.. 33.. الاستقلابي.. 527,621.. والاستقلابية.. 600,354.. واستقلاب.. 670,143.. لاستقلاب.. 738,984.. باستقلاب.. 878,371.. استقلابي.. 888,965.. الإستقلاب.. 950,488.. استقلابا.. 1,257,608.. استقلابه.. 1,257,609.. استقلابها.. 1,257,610.. الأستقلاب.. 1,260,732.. الأستقلابية.. 1,260,733.. إستقلاب.. 1,514,774.. استقلابات.. 1,526,307.. استقلابيا.. 1,526,308.. الاستقلابات.. 1,537,658.. بالاستقلاب.. 1,596,397.. كاستقلاب.. 1,724,178.. للاستقلاب.. 1,763,413.. الإستقلابية.. 2,135,762.. الاستقلابنقص.. 2,147,334.. وأمراضالاستقلاب.. 3,166,051.. واستقلابا.. 3,185,260.. واستقلابية.. 3,185,261.. والاستقلابات.. 3,200,045.. Other lemmas require more complex regular expressions, such as the following, for the noun stem.. fltAn.. :.. /^[wf]?([blk]?|[bk]?Al|[blk]?hAl|ll)fltAn/.. (Note: this includes the colloquial prefix.. hAl-.. 29 forms (Total Freq: 1,728 = 1 every 660,627 words) (Here is a page with my.. الفلتان.. 78,320.. 779.. 661.. فلتان.. 101,135.. 469.. 426.. والفلتان.. 192,043.. 144.. 132.. وفلتان.. 290,316.. 60.. 59.. للفلتان.. 298,073.. 56.. فلتانا.. 351,632.. 41.. 40.. فلتانة.. 367,836.. 49.. 36.. بالفلتان.. 482,666.. لفلتان.. 495,940.. الفلتانة.. 514,485.. بفلتان.. 662,372.. فلتانه.. 665,759.. فلتانها.. 737,270.. الفلتاني.. 892,673.. وفلتانة.. 931,416.. والفلتانة.. 1,080,908.. فالفلتان.. 1,335,814.. فلتانين.. 1,710,064.. والفلتانين.. 1,838,284.. وبفلتان.. 1,853,312.. وفلتانا.. 1,881,266.. الفلتانية.. 2,262,027.. الفلتانين.. 2,262,028.. فلتانات.. 2,809,431.. فلتاني.. 2,809,432.. هالفلتان.. 3,138,700.. وفلتانها.. 3,358,429.. وفلتانهم.. 3,358,430.. وللفلتان.. 3,395,987.. The following regular expressions extract all the inflected forms of the preposition.. fy.. (both versions produce the same output):.. /^[wf]?f[Yy](|h|hA|hmA|hm|hn|k|kmA|km|kn|nA)$/.. /^[wf]?f[Yy]([hk]?|hA|[hk]mA?|[hk]n|nA)$/.. 48 forms (Total Freq: 42,810,237 = 1 every 26 words) (Here is a page with my.. فيها.. 1,222,801.. 632,440.. فيه.. 50.. 1,030,089.. 545,319.. فى.. 1,894,110.. 321,671.. ففي.. 920.. 122,836.. 95,251.. فيهم.. 3,081.. 46,072.. 36,049.. وفى.. 3,103.. 56,930.. 35,850.. وفيه.. 5,674.. 46,993.. 20,247.. فيهما.. 5,837.. 24,842.. 19,709.. وفيها.. 7,115.. 19,925.. 16,027.. فينا.. 7,938.. 18,509.. 14,195.. فيك.. 11,933.. 12,639.. 8,909.. ففى.. 25,202.. 3,981.. 3,560.. ففيه.. 29,518.. 3,705.. 2,874.. فيكم.. 30,851.. 4,485.. 2,712.. ففيها.. 34,545.. 3,087.. 2,317.. فيهن.. 46,295.. 2,632.. 1,518.. وفيهم.. 52,220.. 1,792.. 1,266.. وفينا.. 72,453.. 955.. 750.. وفيهما.. 101,248.. 581.. 425.. وفيك.. 120,370.. 357.. 314.. فىها.. 160,503.. 399.. 185.. فىه.. 168,479.. 340.. 169.. ففيهما.. 170,194.. 204.. 166.. ففيهم.. 186,930.. 158.. 139.. وفيكم.. 190,969.. 213.. 133.. فيكن.. 218,127.. 130.. وفيهن.. 240,648.. 99.. 85.. فيكما.. 291,082.. 83.. 58.. ففيك.. 360,477.. 39.. 38.. ففينا.. 456,896.. ففيهن.. 562,437.. فىنا.. 606,172.. فىهم.. 687,798.. وفىها.. 689,782.. وفيكن.. 747,527.. وفىه.. 769,046.. فىهما.. 817,607.. فىك.. 869,451.. ففيكم.. 953,608.. ففىه.. 1,340,653.. ففىها.. 1,340,654.. فىكم.. 1,715,068.. ففيكن.. 2,805,347.. فىهن.. 2,826,754.. وفىهما.. 3,359,666.. وفىهن.. 3,359,667.. Some contamination from other lemmas does occur.. Some of the homographs, such as.. ,.. are easy to spot, but others are quite unexpected.. For example,.. is also the Armenian proper name Vigen (I thank my Arabic-L colleagues for pointing this out), and.. is also the Belgian firm FINA and a rare spelling of Vienna.. Unexpected contamination usually shows up when a concordance is generated (see.. I can anticipate some of the extraneous lemmas by running the wordlist through my morphological parser (see.. ) before generating the concordance.. | WORD FREQUENCY COUNTS |..

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  • Title: Arabic Concordances
    Descriptive info: ARABIC CONCORDANCES.. I experimented over several years with various KWIC layouts for Arabic, using Arabic script and transliteration, monitors of various sizes and resolutions, plain text and formatted text, ASCII editors, Excel, IE and Word.. After many trials I settled on a configuration using Word RTF files and transliteration, which allows me to display over 226 mono-spaced characters per line: I usually allocate 30 for the reference, 96 for the context before the KWIC, and 100 for the context that follows it.. The font I use is set to a point size of 7, and the zoom view in Word is set at 112%.. These settings and two tab stop values are all in the RTF file header, which I generate automatically along with the concordance output.. By using Word file format for my concordances I can start composing my dictionary entries in the concordance file itself.. As I identify the different lemmas, senses and collocations, I sort and re-group them by  ...   hits, so it's often worthwhile to include optional short vowels and diacritics in one's search, even though it slows down searching tremendously).. A search for the noun stem.. , for example, is specified as:.. /(^|[^$alpha])[$vds]*[wf]?[$vds]*([blk]?[$vds]*|[bk]?[$vds]*A[$vds]*l[$vds]*|[blk]?[$vds]*h[$vds]*A[$vds]*l[$vds]*|l[$vds]*l[$vds]*)f[$vds]*l[$vds]*t[$vds]*A[$vds]*n/.. where.. $alpha = PJG'| }AbptvjHxd*rzs$SDTZEg_fqklmnhwYyFNKaui~o.. $vds = aiuo~_NFK.. (Here is a page with my.. Here are two versions of the same concordance prior to editing:.. concordance of.. sorted by text after the KWIC:.. fltAn-A.. rtf.. (356k).. sorted by text before the KWIC:.. fltAn-B.. Collocations readily show up in both versions:.. in fltAn_A: (N+Adj).. fltAn Amny.. fltAn AxlAqy.. fltAn AElAmy.. ; (idafa).. fltAn AlAmwr.. fltAn AlAsEAr.. fltAn AlwDE AlAmny.. ; (misc.. phrases).. AlfltAn wAltsyb.. AlfltAn wAlfwDY.. ; (proper noun).. AlfltAn bn EASm.. in fltAn_B: (idafa).. HAlp/HAl AlfltAn.. DbT AlfltAn.. mnE AlfltAn.. Altsyb wAlfltAn.. wDE Hd llfltAn.. AlfwDY wAlfltAn.. ; (idiom).. flAn Aw fltAn.. And here is a sample concordance with re-arranged lines and inserted temporary glosses (i.. e.. , this is lexicography in progress):.. mfSly-B.. (562k).. | CONCORDANCING |..

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  • Title: Arabic Morphology Analysis
    Descriptive info: ARABIC MORPHOLOGY ANALYSIS.. Background.. The Perl skills I developed for corpus compilation, lexical statistics and concordancing eventually allowed me to write my own morphological parser.. I had participated in the design of morphological parsers in the early 1990's at Alpnet and the CRL at New Mexico State University, and I used this experience when designing my own system.. My goal was to design a system in which the morphotactics and morphophonemic rules were built directly into the lexicon itself.. Therefore, instead of compiling numerous lexicons of prefix and suffix morphemes (for which I would have to specify concatenation rules) I compiled a single lexicon of all prefixes including all valid concatenations and I also compiled a corresponding unified lexicon for suffixes.. The morphophonemic rules were treated simply as orthographic variation and addressed by means of additional dictionary entries.. Although an Arabic parser with unvocalized lexicon entries can be built and would be fully functional, I include short vowels and diacritics in my lexicons because without them the lexicon is extremely difficult to maintain and the analysis output is also difficult to interpret.. I was asked by several Prague linguists to add lemma ID's to each entry, and I was encouraged by Mark Liberman of the LDC to build the lemma ID string by using the actual citation form and a unique number.. Since complying with these requests I have found the lemma ID string to be quite useful, such as when reading the analyses of irregular Form I verbs, or for quickly locating lexicon entries.. My primary goal in building a morphological parser was lexical tagging or identification for use in lexicography, especially lemmatization rather than morphological analysis per se.. In my approach I use word stems rather than root and pattern morphemes to identify lexical items.. (The information on root and pattern morphemes could be added to each stem entry if this were desired).. Here is the morphology analysis output of the first 30 words in my Aug.. 2002 wordlist (.. 30-words.. txt.. - 11k), using a version of the lexicon dated Nov.. 1, 2002.. (Here's a link to my.. table).. Arabic Morphology Analysis Algorithm.. My Arabic morphology analysis is based on these assumptions:.. words are composed of three elements: prefix, stem, and suffix.. the prefix can have 0-4 characters.. the stem can have 1-infinite characters.. the suffix can have 0-6 characters.. Given these rules, an Arabic word can be segmented as follows (using.. wbAlErbyp.. as an example):.. Prefix.. Stem.. Suffix.. wbAlErby.. p.. wbAlErb.. yp.. wbAlEr.. byp.. wbAlE.. rbyp.. wbAl.. Erbyp.. wbA.. lErbyp.. w.. bAlErbyp.. bAlErby.. bAlErb.. bAlEr.. bAlE.. bAl.. bA.. wb.. AlErbyp.. AlErby.. AlErb.. AlEr.. AlE.. Al.. A..  ...   Earabiy~.. N/ap.. Arab pos Earabiy~/NOUN /pos.. Earab.. N.. Arabs pos Earab/NOUN /pos.. Arab pos Earabiy~/ADJ /pos.. Arab pos Earab/ADJ /pos.. ;; Earabiy~_2.. N-ap.. Arabic;Arab pos Earabiy~/ADJ /pos.. ;; Earabiy~_3.. N0.. Arabi pos Earabiy~/NOUN_PROP /pos.. ;; Earabiy~ap_1.. NapAt.. Arabic (language) pos Earabiy~/NOUN /pos.. And here are some sample entries from the dictionary of suffixes:.. ap.. NSuff-ap.. [fem.. sg.. ] pos +ap/NSUFF_FEM_SG /pos.. Ak.. Aka.. NSuff-Ah.. your two pos +A/NSUFF_MASC_DU_NOM+ka/POSS_PRON_2MS /pos.. Aki.. your two pos +A/NSUFF_MASC_DU_NOM+ki/POSS_PRON_2FS /pos.. tmwny.. otumuwniy.. PVSuff-th.. you [masc.. pl.. ] verb me pos +tum/PVSUFF_SUBJ:2MP+niy/PVSUFF_DO:1S /pos.. If all three word elements (prefix, stem, suffix) are found in their respective lexicons, we then use their respective morphological categories (the string in column 3) to determine whether they are compatible.. We ask:.. is the morphological category of the prefix compatible with the morphological category of the stem? (i.. , is the combination pair found in the list of compatible prefix-stem morphological categories?).. if so, is the morphological category of the prefix compatible with the morphological category of the suffix? (i.. , is the combination found in the list of compatible prefix-suffix morphological categories?).. if so, is the morphological category of the stem compatible with the morphological category of the suffix? (i.. , is the combination found in the list of compatible stem-suffix morphological categories?).. If the answer to the last question is yes then the morphological analysis is valid.. Example:.. INPUT STRING: æÕÝå.. LOOK-UP WORD: wSfh.. SOLUTION 1: (waSafahu) [waSaf-i_1] waSaf/VERB_PERFECT+a/PVSUFF_SUBJ:3MS+hu/PVSUFF_DO:3MS.. (GLOSS): + describe/characterize + he/it verb it/him.. SOLUTION 2: (waSafahu) [waSaf-i_1] waSaf/VERB_PERFECT+a/PVSUFF_SUBJ:3MS+hu/PVSUFF_DO:3MS.. (GLOSS): + prescribe/give a prescription to + he/it verb it/him.. SOLUTION 3: (waSofh) [waSof_1] waSof/NOUN+hu/POSS_PRON_3MS.. (GLOSS): + description/portrayal/characterization + its/his.. SOLUTION 4: (waSofh) [waSof_2] waSof/NOUN+hu/POSS_PRON_3MS.. (GLOSS): + characteristic + its/his.. SOLUTION 5: (waSaf~ahu) [Saf~-u_1] wa/CONJ+Saf~/VERB_PERFECT+a/PVSUFF_SUBJ:3MS+hu/PVSUFF_DO:3MS.. (GLOSS): and + arrange/classify + he/it verb it/him.. SOLUTION 6: (waSaf~h) [Saf~_1] wa/CONJ+Saf~/NOUN+hu/POSS_PRON_3MS.. (GLOSS): and + line/row/class + its/his.. Solution #1 was found to be valid because:.. All 3 components.. (null)+wSf+h.. exist in their respective lexicons (note that there is a literal entry for the null prefix):.. (null).. Pref-0.. wSf.. waSaf.. PV.. describe;characterize.. h.. ahu.. PVSuff-ah.. he/it verb it/him pos +a/PVSUFF_SUBJ:3MS+hu/PVSUFF_DO:3MS /pos.. The morphological categories of all 3 components are listed as compatible pairs in the relevant compatibility tables:.. Pref-0 PV.. (listed in the table of compatible prefix-stem morphological categories).. PV PVSuff-ah.. (listed in the table of compatible stem-suffix morphological categories).. Pref-0 PVSuff-ah.. (listed in the table of compatible prefix-suffix morphological categories).. Solution #6 was found to be valid because:.. All 3 components.. w+Sf+h.. exist in their respective lexicons:.. wa.. Pref-Wa.. and pos wa/CONJ+ /pos.. Sf.. Saf~.. Ndu.. line;row;class.. NSuff-h.. its/his pos +hu/POSS_PRON_3MS /pos.. Pref-Wa Ndu.. Ndu NSuff-h.. Pref-Wa NSuff-h.. | MORPHOLOGY ANALYSIS |..

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  • Title: Arabic Lexicon
    Descriptive info: (under construction).. | ARABIC LEXICON..

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  • Title: Buckwalter Arabic Transliteration
    Descriptive info: ARABIC TRANSLITERATION.. I developed my transliteration system before XML days.. To make it XML-friendly I would:.. replace.. with.. I.. (for.. hamza.. -under-.. alif.. O.. -over-.. the A is already used for bare.. W.. -on-.. waw.. Arabic Windows.. Unicode Value and Unicode Name.. '.. C1.. U+0621 ARABIC LETTER HAMZA.. |.. C2.. U+0622 ARABIC LETTER ALEF WITH MADDA ABOVE.. C3.. U+0623 ARABIC LETTER ALEF WITH HAMZA ABOVE.. C4.. U+0624 ARABIC LETTER WAW WITH HAMZA ABOVE.. C5.. U+0625 ARABIC LETTER ALEF WITH HAMZA BELOW.. }.. C6.. U+0626 ARABIC LETTER YEH WITH HAMZA ABOVE.. C7.. U+0627 ARABIC LETTER ALEF.. b.. C8.. U+0628 ARABIC LETTER BEH.. C9.. U+0629 ARABIC LETTER TEH MARBUTA.. t.. CA.. U+062A ARABIC LETTER TEH.. v.. CB.. U+062B ARABIC LETTER THEH.. j.. CC.. U+062C ARABIC LETTER JEEM.. H.. CD.. U+062D ARABIC LETTER HAH.. x.. CE.. U+062E ARABIC LETTER KHAH.. d.. CF.. U+062F ARABIC LETTER DAL.. *.. D0.. U+0630 ARABIC LETTER THAL.. r.. D1.. U+0631 ARABIC LETTER REH.. z.. D2.. U+0632 ARABIC LETTER ZAIN.. s.. D3.. U+0633 ARABIC LETTER SEEN.. $.. D4.. U+0634 ARABIC LETTER SHEEN.. S.. D5.. U+0635 ARABIC LETTER SAD.. D.. D6..  ...   E4.. U+0646 ARABIC LETTER NOON.. E5.. U+0647 ARABIC LETTER HEH.. E6.. U+0648 ARABIC LETTER WAW.. Y.. EC.. U+0649 ARABIC LETTER ALEF MAKSURA.. y.. ED.. U+064A ARABIC LETTER YEH.. F.. F0.. U+064B ARABIC FATHATAN.. F1.. U+064C ARABIC DAMMATAN.. K.. F2.. U+064D ARABIC KASRATAN.. a.. F3.. U+064E ARABIC FATHA.. u.. F5.. U+064F ARABIC DAMMA.. i.. F6.. U+0650 ARABIC KASRA.. ~.. F8.. U+0651 ARABIC SHADDA.. o.. FA.. U+0652 ARABIC SUKUN.. `.. U+0670 ARABIC LETTER SUPERSCRIPT ALEF.. {.. U+0671 ARABIC LETTER ALEF WASLA.. P.. 81.. U+067E ARABIC LETTER PEH.. J.. 8D.. U+0686 ARABIC LETTER TCHEH.. V.. U+06A4 ARABIC LETTER VEH.. G.. 90.. U+06AF ARABIC LETTER GAF.. The full Arabic character set can be viewed at the.. Unicode.. website:.. Arabic: U+0600 to U+06FF.. (PDF format).. Arabic Presentation Forms-A: U+FB50 to U+FDFF.. Arabic Presentation Forms-B: U+FE70 to U+FEFF.. TITUS page for U+0600 through U+06FF.. displays the actual characters in your browser (UTF-8 encoding).. You can test your web browser's Arabic Unicode support at.. Alan Wood s Unicode Resources.. website.. The Microsoft developer website has a useful table of the.. ISO 8859-6.. code pages and their corresponding Unicode values..

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  • Title:
    Descriptive info: INPUT STRING: ãä LOOK-UP WORD: mn SOLUTION 1: (min) [min_1] min/PREP (GLOSS): + from + SOLUTION 2: (man) [man_1] man/REL_PRON (GLOSS): + who/whom + SOLUTION 3: (man) [man_2] man/INTERROG_PART (GLOSS): + who/whom + SOLUTION 4: (man~a) [man~-u_1] man~/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + bestow/grant + he/it.. SOLUTION 5: (man~) [man~_1] man~/NOUN (GLOSS): + grace/favor + INPUT STRING: Ýí LOOK-UP WORD: fy SOLUTION 1: (fiy) [fiy_1] fiy/PREP (GLOSS): + in + SOLUTION 2: (fiy~a) [fiy_1] fiy/PREP+~a/PRON_1S (GLOSS): + in + me SOLUTION 3: (fiy) [fiy_2] Viy/ABBREV (GLOSS): + V.. + INPUT STRING: Úáì LOOK-UP WORD: ElY SOLUTION 1: (EalaY) [EalaY_1] EalaY/PREP (GLOSS): + on/above + SOLUTION 2: (Ealay~a) [EalaY_1] Ealay/PREP+~a/PRON_1S (GLOSS): + on/above + me SOLUTION 3: (Ealiy~) [Ealiy~_1] Ealiy~/ADJ (GLOSS): + supreme/high + SOLUTION 4: (Ealiy~) [Ealiy~_2] Ealiy~/NOUN_PROP (GLOSS): + Ali + INPUT STRING: Úä LOOK-UP WORD: En SOLUTION 1: (Ean) [Ean_1] Ean/PREP (GLOSS): + from/about + SOLUTION 2: (Ean~a) [Ean~-ui_1] Ean~/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + come to mind/occur + he/it.. INPUT STRING: Çä LOOK-UP WORD: An SOLUTION 1: (>an) [>an_1] >an/SUB_CONJ (GLOSS): + to + SOLUTION 2: (>an~a) [>an~a_1] >an~a/SUB_CONJ (GLOSS): + that + SOLUTION 3: (.. an~a) [>an~-i_1] >an~/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + complain/moan + he/it.. SOLUTION 7: (|na) [|n-ui_1] |n/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + arrive/approach + he/it.. SOLUTION 8: (|n) [|n_1] |n/NOUN (GLOSS): + time/moment + SOLUTION 9: (.. SOLUTION 2: (.. aliy) [waliy-i_1] >a/IV1S+liy/VERB_IMPERFECT (GLOSS): I + follow/come after + INPUT STRING: ÇáÐí LOOK-UP WORD: Al*y SOLUTION 1: (Al~a*iy) [Al~a*iy_1] Al~a*iy/REL_PRON (GLOSS): + which/who/whom [masc.. ] + SOLUTION 2: (>alo*aY) [la*iy-a_1] >a/IV1S+lo*aY/VERB_IMPERFECT (GLOSS): I + adhere/cleave + INPUT STRING: ãÚ LOOK-UP WORD: mE SOLUTION 1: (maEa) [maEa_1] maEa/PREP (GLOSS): + with + INPUT STRING: åÐÇ LOOK-UP WORD: h*A SOLUTION 1: (h`*A) [h`*A_1] h`*A/DEM_PRON_MS (GLOSS): + this [masc.. ] + INPUT STRING: åÐå LOOK-UP WORD: h*h SOLUTION 1: (h`*ihi) [h`*A_1] h`*ihi/DEM_PRON_F (GLOSS): + this/these + INPUT STRING: ÈÚÏ LOOK-UP WORD: bEd SOLUTION 1: (baEoda) [baEoda_1] baEoda/PREP (GLOSS): + after + SOLUTION 2: (baEodi) [baEoda_1] baEodi/PREP (GLOSS): + after + SOLUTION 3: (baEodu) [baEodu_1] baEodu/ADV (GLOSS): + afterward/later/(not) yet + SOLUTION 4: (baEuda) [baEud-u_1] baEud/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + be distant + he/it.. SOLUTION 5: (buEod) [buEod_1] buEod/NOUN (GLOSS): + dimension/distance + SOLUTION 6: (biEad~) [Ead~_1] bi/PREP+Ead~/NOUN (GLOSS): by/with + counting/calculating + INPUT STRING: Èíä LOOK-UP WORD: byn SOLUTION 1: (bayona) [bayona_1] bayona/PREP (GLOSS): + between/among + SOLUTION 2: (bayoni) [bayona_1] bayoni/PREP (GLOSS): + between/among + SOLUTION 3: (bay~ana) [bay~an_1] bay~an/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + declare/demonstrate  ...   (GLOSS): + eat + you.. SOLUTION 2: (kul~) [kul~_1] kul~/NOUN (GLOSS): + every/all/each one + INPUT STRING: Ðáß LOOK-UP WORD: *lk SOLUTION 1: (*`lika) [*`lika_1] *`lika/DEM_PRON_MS (GLOSS): + that + SOLUTION 2: (*ul~ka) [*ul~_1] *ul~/NOUN+ka/POSS_PRON_2MS (GLOSS): + humiliation/dishonor + your SOLUTION 3: (*ul~ki) [*ul~_1] *ul~/NOUN+ki/POSS_PRON_2FS (GLOSS): + humiliation/dishonor + your INPUT STRING: ßÇä LOOK-UP WORD: kAn SOLUTION 1: (kAna) [kAn-u_1] kAn/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + was/were + he/it.. SOLUTION 2: (ka|n) [|n_1] ka/PREP+|n/NOUN (GLOSS): like/such as + time/moment + INPUT STRING: áã LOOK-UP WORD: lm SOLUTION 1: (lam) [lam_1] lam/NEG_PART (GLOSS): + not + SOLUTION 2: (lima) [lima_1] lima/INTERROG_PART (GLOSS): + why + SOLUTION 3: (lam~a) [lam~-u_1] lam~/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + collect/put in order + he/it.. INPUT STRING: æÞÇá LOOK-UP WORD: wqAl SOLUTION 1: (waqAla) [qAl-u_1] wa/CONJ+qAl/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): and + said + he/it.. INPUT STRING: ÞÏ LOOK-UP WORD: qd SOLUTION 1: (qad) [qad_1] qad/VERB_PART (GLOSS): + (has/have) + SOLUTION 2: (qad) [qad_2] qad/VERB_PART (GLOSS): + may/might + SOLUTION 3: (qid~) [qid~_1] qid~/NOUN (GLOSS): + strap/thong + SOLUTION 4: (qad~) [qad~_1] qad~/NOUN (GLOSS): + size/physique + SOLUTION 5: (qud~) [qud~_1] qud~/NOUN (GLOSS): + codfish + INPUT STRING: ÑÆíÓ LOOK-UP WORD: r}ys SOLUTION 1: (ra}iys) [ra}iys_1] ra}iys/NOUN (GLOSS): + president/head/chairman + INPUT STRING: ÇáÚÇã LOOK-UP WORD: AlEAm SOLUTION 1: (AlEAm~) [EAm~_1] Al/DET+EAm~/ADJ (GLOSS): the + general/common/public + SOLUTION 2: (AlEAm) [EAm_1] Al/DET+EAm/NOUN (GLOSS): the + year + INPUT STRING: ÍíË LOOK-UP WORD: Hyv SOLUTION 1: (Hayovu) [Hayovu_1] Hayovu/CONJ (GLOSS): + where/whereby/since/given that + INPUT STRING: ÞÈá LOOK-UP WORD: qbl SOLUTION 1: (qabola) [qabola_1] qabola/PREP (GLOSS): + before + SOLUTION 2: (qaboli) [qabola_1] qaboli/PREP (GLOSS): + before + SOLUTION 3: (qabolu) [qabolu_1] qabolu/ADV (GLOSS): + before/prior + SOLUTION 4: (qibal) [qibal_1] qibal/NOUN (GLOSS): + (on the) part of + SOLUTION 5: (qabila) [qabil-a_1] qabil/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + accept/receive/approve + he/it.. SOLUTION 6: (qab~ala) [qab~al_1] qab~al/VERB_PERFECT+a/PVSUFF_SUBJ:3MS (GLOSS): + kiss + he/it.. INPUT STRING: Çáíæã LOOK-UP WORD: Alywm SOLUTION 1: (Aloyawoma) [yawom_3] Aloyawoma/ADV (GLOSS): + today + SOLUTION 2: (Alyawom) [yawom_1] Al/DET+yawom/NOUN (GLOSS): the + day + SOLUTION 3: (Alyawom) [yawom_4] Al/DET+yawom/NOUN_PROP (GLOSS): the + Youm + INPUT STRING: æÝí LOOK-UP WORD: wfy SOLUTION 1: (wafaY) [wafaY-i_1] wafaY/VERB_PERFECT+(null)/PVSUFF_SUBJ:3MS (GLOSS): + fulfill/carry out + he/it.. SOLUTION 2: (waf~aY) [waf~aY_1] waf~aY/VERB_PERFECT+(null)/PVSUFF_SUBJ:3MS (GLOSS): + satisfy/fulfill + he/it.. SOLUTION 3: (wafiy~) [wafiy~_1] wafiy~/ADJ (GLOSS): + faithful/complete + SOLUTION 4: (wafiy) [fiy_1] wa/CONJ+fiy/PREP (GLOSS): and + in + SOLUTION 5: (wafiy~a) [fiy_1] wa/CONJ+fiy/PREP+~a/PRON_1S (GLOSS): and + in + me..

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