# Guidelines for Annotation of Arabic Dialectness

Nizar Habash, Owen Rambow, Mona Diab and Reem Kanjawi-Faraj

Center for Computational Learning Systems

Columbia University

New York, NY, USA

{habash,rambow,diab}@ccls.columbia.edu

## Abstract

The Arabic language is a collection of variants with phonological, morphological, lexical, and syntactic differences comparable to those among the Romance languages. However, throughout the Arab world, the standard written language is the same, Modern Standard Arabic (MSA). MSA is based on Classical Arabic and is itself not a native spoken language. MSA is used in some official spoken communication, such as newscasts, parliamentary debates, etc. Other forms of Arabic (generally referred to as “dialects” of MSA) are what people use for daily spoken communication. In this paper, we focus on the issue of creating standard annotation guidelines identifying dialect switching between MSA and at least one dialect in written text. These guidelines can form the basis for the annotation of large collections of data that will be used for training and testing automatic approaches to dialect identification and automatic processing of Arabic which exhibits dialect switching. We report on some initial annotation experiments: we discuss statistical distributions of labels in a small corpus (~19K words) that we annotated according to the guidelines and present inter-annotator agreement results.

## 1. Introduction

The Arabic language is a collection of variants with phonological, morphological, lexical, and syntactic differences comparable to those among the Romance languages (see for example **جَلْوة** 1959, Butros, 1973, Omar 1976, السامراتي 1981, Brustad 2000, Bateson 2003, Holes 2004; for computationally oriented summaries of the linguistic situation, see Habash 2006, Diab and Habash 2007). However, throughout the Arab world, the standard written language is the same, Modern Standard Arabic (MSA, **فَصْحُ السَّمَاعُ**),<fnref n="1" /> also used in some official spoken communication (newscasts, parliamentary debates, etc.). MSA is based on Classical Arabic and is itself *not* a native spoken language. Other forms of Arabic (generally referred to as “dialects” of Arabic) are what people use for daily spoken communication. In unofficial written communication, in particular in the now growing electronic media, often ad-hoc transcriptions of dialects are used. Arabic dialects are usually divided into four geographic groups: Gulf (including all dialects of the Arabian Peninsula and Iraqi), Levantine (including Syrian, Lebanese, Palestinian), Egyptian (including Libyan and Sudanese) and Maghreb (covering all dialects found West of Libya). Other factors such as the bedouin/sedentary distinction and the distinction between rural (village) and

urban communities also define some sub-dialectal variations.

MSA and the dialects thus form a prototypical case of “diglossia” (Ferguson 1959). In a diglossic situation, a “high” language is used in public or prestigious communicative situations (media, government) and is written, while a “low” language is used in private communicative situations (family, daily life) and is usually not written. The two forms co-exist; all speakers master the low form, and most speakers also master the high language to a greater or lesser extent. Arabic conforms to the original definition of diglossia given by Ferguson (1959) in that the two forms are closely related.

In diglossic linguistic situations, one frequently finds cases of code switching, i.e., the use of one or more languages, language variants, or dialects in one discourse, and often within one sentence. We will use the term “dialect switching” in this paper to mean the use of two or more variants of Arabic in one discourse. Dialect switching is well attested in Arabic; see for example (Badawi and Hinds 1986) for a proposal to divide Arabic into five levels, each characterized by the extent of the contributions from MSA (or classical Arabic), from dialectal Arabic, and from foreign languages. Different levels correspond to different sociolinguistic parameters, including education level of the discourse participants, as well as discourse purpose and setting. We adopt the assumption that different types of dialect switching happen in different discourses.

In this paper we focus on the issue of creating standard annotation guidelines identifying dialect switching between MSA and at least one dialect in written text (including transcripts of spoken Arabic). These guidelines can then be used to annotate large collections of data that will be used for training and testing natural language

<footnote n="1">¹ All Arabic transliterations are provided in the Habash-Soudi-Buckwalter transliteration scheme (Habash et al., 2007). This scheme extends Buckwalter's transliteration scheme (Buckwalter, 2004) to increase its readability while maintaining the 1-to-1 correspondence with Arabic orthography as represented in standard encodings of Arabic, i.e., Unicode, CP-1256, etc. The following are the only differences from Buckwalter's scheme (indicated in parentheses): Ā (|), Ā (>), ḥ (%), Ā! (<), ɣ̈ (}), ḫ (p), θ̈ (v), ḍ (★), š (ש) , Ḍ (Z), ṣ (E), γ̈ (g), ý (Y), ā (F), ū (N), ī (K).</footnote>
processing (NLP) tools which can identify when dialect
switching occurs in a document, and which dialects are
involved. This is important, as dialect switching is quite
common – as we discuss in Section 3.1, even in edited
newswire (which should be all MSA), we find 3.8% of
segments to have dialectal influence, and this percentage
goes up to 72.3% for broadcast conversations on television
(in which participants are usually expected to
communicate in MSA as well). The problem is that most
existing NLP tools have been developed for pure MSA, for
example morphological taggers (Habash and Rambow
2005, Diab et al 2007) or parsers (Bikel 2002, Maamouri
et al. 2004a, Maamouri et al. 2006). To have these tools
handle Arabic as it is actually produced (namely, with
dialect switching), we need to be able not only to handle
pure MSA and pure dialectal data,<fnref n="2" /> but we also need to
develop computational models that can detect and
incorporate dialect switching. The proposed annotation
guidelines will provide a first step towards creating such
computational models, and, generally, towards a broad
empirical study of dialect switching in Arabic.

The task of defining annotation guidelines for Arabic
dialect switching (or in fact any dialect switching) is
complex, because the boundaries between MSA and a
dialect are not well defined. If we are studying code
switching between, for example, Arabic and English, we
can almost always determine from which language a word,
a morpheme, even the pronunciation is taken. The only
major methodological difficulty is distinguishing
borrowings (words that have entered the general
vocabulary of language A from language B, such as
algebra or oud in English) from nonce borrowings (words
from language B used spontaneously by a speaker while
speaking predominantly in language A). In the case of
dialect switching, we cannot readily identify borrowings
(whether nonce borrowings or regular borrowings) at all.
Especially complicating is the fact that Arabic
orthography often omits all short vowel diacritics, which
may otherwise distinguish between different dialects.

In the rest of this paper we present and exemplify our
guidelines (Section 2) and discuss a preliminary study
annotating a small corpus with these guidelines and
computing inter-annotator agreement (Section 3).

## 2. Dialect Switching Annotation

When investigating dialect switching (and code switching
in general), we can distinguish several (potentially)
orthogonal levels of annotation: phonology, morphology,
lexicon, and syntax. Since we are interested in written
language (including transcribed speech) for the sake of
this paper, we omit the phonological level and replace it by
orthography. One approach to annotation would be to
annotate each of the levels separately. However, in this
paper, we group the annotation decisions into two
decisions for the word and the segment level (plus one
additional annotation decision for the document level).

The decisions at the word and segment level are judgments
on a precisely defined scale, which we refer to as the
“dialectness” scale.

Given the difficulty of determining whether a word (or a
morpheme) is a borrowing at all in a dialect code
switching situation, we have decided to *always* code a
word (or morpheme) which can be construed in context as
being an MSA word (or morpheme) as MSA. Put
differently, our default assumption is that the text is MSA,
and we are annotating only clear evidence of dialect
influence.

Our approach – two different judgments at the word and
segment levels – represents a calculated shortcut, in that it
does not provide detailed information at the orthographic,
morphological, lexical, and syntactic levels. Instead,
based on a preliminary study, we make the assumption that
certain combinations are so rare that a simplified
annotation scheme is warranted. We acknowledge that a
more complex annotation scheme may be required for
certain types of studies or computational needs.

## 2.1 Word-Level Dialect Annotation

Decisions at the word level reflect on orthography,
morphology and the lexicon. We annotate the dialectness
of each word as a choice among four levels. To determine
the correct level, we must first determine the *lexeme* and
the *inflectional morphemes* of the word. The two notions
are closely related. The lexeme is an abstraction over all
possible inflectional morphemes. For example, *kitAb
*کتاب* *kitAb* ‘book’, *وکتابهم* *AlkitAbAn* ‘the two books’, *wabikutubikum* ‘and with your books’ all are variants of
the same lexeme, which, like all nouns, is usually referred
to with the citation form of the singular masculine, *کتاب
kitAb* ‘book’. Although the word-level annotation task is
concerned with the ‘words’, the context is still relevant to
some degree. The annotators need to determine what the
meaning of the complete segment is first before
proceeding to annotate the different words. This is
important since certain words may potentially mean
different things in different contexts and deserve different
treatments. For example, the word *بایع* *bAyç* in *بایع سیار تک* *bAyic say~Artak inta?* ‘are you selling your car?’
clearly means ‘selling’ (*bAyic*) not ‘offered allegiance’
(*bAyaç*). The first reading is clearly dialectal (from *بایع
bAyic*) whereas the second reading is MSA. Thus, in this
context, this word would be coded as being a dialectal
lexeme (since there is no plausible MSA reading).

The following are the four levels for word dialectness
annotation:

**Word Level 0** is used for *pure MSA* words. These words
are standard MSA lexemes with the contextually
appropriate MSA inflectional morphology; they have
standard MSA orthography with no typographical errors,
e.g., *yaktubuwn* ‘they write’, *AlmasAjid*
*أعيادكم*, *سیقوم* *sayaquwm* ‘he will rise’,
*أعيادهم*

<footnote n="2">² For example, see (Habash and Rambow 2006) and (Chiang et al. 2006) for NLP tools for the dialects.</footnote>
AṣyAdukum ‘your holidays’, بغداد baydAd ‘Baghdad’, سيليكون siyliykawn ‘silicon’, and واشنطن wAšinTuwn ‘Washington’.

**Word Level 1** refers to MSA *with non-standard orthography*. These words are standard MSA lexemes with the contextually appropriate MSA inflectional morphology, but something is strange in the spelling: either a non-standard spelling possibly inspired by non-standard pronunciation, regional variation in spelling, or typographical errors. We do not ask the annotators to choose one of these options as they are often hard to distinguish. The annotators also do not indicate the correct spelling. Examples include مساجد masAjiḍ (instead of مساجد masAjid ‘mosques’, presumably a spelling error), قسطن fusTAn (instead of فستان fustAn ‘dress’, presumably dialectal spelling) and هذا hadA (instead of هذا haḍA ‘this’ dialectal spelling or spelling error).

**Word Level 2** indicates an MSA *word with dialect morphology*. These words are standard MSA lexemes but they have at least one morpheme which is clearly a dialectal morpheme (from any of the dialects). A very common example of this is the use of the +ب b+ prefix in conjunction with an otherwise entirely acceptable MSA verb, for example *بيدهب biyaḍhab* ‘he goes’. Note that the +ب b+ prefix can appear with nouns in MSA but not with verbs. A common spelling of the Levantine/Egyptian verb ‘I write’ is *بكتb baktub*; although this word looks like the MSA *bikutub* ‘in books’, the syntactic and semantic contexts are used to disambiguate. Examples of other dialectal morphemes often found in conjunction with MSA words include +خ Ha+ (Egyptian/Levantine ‘future tense’), +خ ᶅa+ (Levantine preposition ‘on/to’), +د da+ (Iraqi ‘present tense’), +ك ka+ (Moroccan ‘present tense’), +خ γa+ (Moroccan ‘future tense’), +ش++ρ ma++š (Egyptian/ Levantine negation circumfix) and +ش š+ (Iraqi question marker).

**Word Level 3** indicates a *dialect lexeme*. These words are words which clearly would never be used in written or spoken MSA by an educated speaker/writer. This level does not include orthographic variants of MSA words, these are coded as Word Level 1. Prime examples are the negation marker مش miš ‘no/not’ and its variants and other dialectal morphemes that can be spelled separately from the word: ᶜ Ha (Egyptian/Levantine ‘future tense’), ῃ ᶅa (Levantine preposition ‘on/to’), and ṁm ṣam (Levantine verbal particle marking progressiveness), etc. Other examples of purely dialectal lexemes include: بزونة baz~unah (Iraqi for ‘cat’). Dialectal lexemes that have MSA homophones are also marked if the context shows that the word is clearly dialectal, for example عافية ṣAfyaḥ (Moroccan for ‘fire’ but MSA/Levantine for ‘health’). Note that at this level, orthography is irrelevant, as there is no standard orthography for the dialects anyway. We also do not consider morphology: a dialect word with MSA-only morphology is also coded as Word Level 3.

## 2.2 Segment-Level Dialect Annotation

Beyond word annotation, the annotators are asked to judge the whole segment (sentence/utterance) all at once in terms of the quality of the MSA. This annotation is necessary to address cases that at the word level seem all MSA, but it is clear that the sentence is dialectal or mixed because of lexical issues involving multi-word lexemes, or because of syntax. The judgment is on a scale from 0 to 4.

**Segment Level 0** is defined to be *perfect MSA*.

**Segment Level 1** is *imperfect MSA*. Here, the source is trying to produce MSA, but some dialectal phenomena are sneaking in (dialectally inspired orthography revealing pronunciation, some dialectal morphology, incorrect case or mood, incorrect subject-verb agreement, or perhaps an isolated dialectal lexeme). A segment cannot be Segment Level 1 if there are more than minimal number of words of Word Level 3 – in practice, we have yet to define this threshold.

**Segment Level 2** is *Arabic with full dialect switching*. It is not clear whether the writer is aiming for writing in MSA, or in dialect. A segment cannot be Segment Level 2 if all words are Word Level 2 or Word Level 3.

**Segment Level 3** refers to *dialect with MSA incursions*. The source is producing dialectal Arabic, but uses some clichés or words clearly borrowed from MSA. A segment cannot be Segment Level 3 if all words are Word Level 3.

**Segment Level 4** is used to mark *pure Dialect*. Here, the writer is producing pure dialectal Arabic. A segment of Segment Level 4 has, in general, at least one word of Word Level 2 or Word Level 3.

## 2.3 Source-Level Annotation: Home Dialect of Speaker/Writer

After the previous two annotations are done, the annotator may have a reasonably good idea of the home dialect of the speaker/writer. This guess will be marked. The annotators are encouraged to use any knowledge they can muster, e.g., the language used, the words used, the topic, the home country of the newsgroup or TV station, etc. We specify a hierarchy of specific names for dialect regions (such as Maghreb>Tunisian and Levantine>Palestinian) and sub-dialectal features (such Urban or Bedouin). This allows a degree of reasonable approximation in case of doubt.

## 3. Annotation Experiments

In this section, we present some preliminary results on an annotation task using our guidelines. We first describe the annotation and detail statistics on the distribution of labels across different genres. Then we present inter-annotator agreement results on a portion of the annotated data.
## 3.1 Corpus Annotation

We annotated a small corpus of 59 documents (19,160 words) in four genres: newswire (NW), web text (WT), broadcast news (BN) and broadcast conversation (BC). The annotation was done as part of MT error analysis research (Kirchhoff et al. 2007), and our corpus choice was dictated by this task. NW and WT were naturally occurring text as opposed to BN and BC which have been transcribed by the Linguistic Data Consortium (LDC).<fnref n="3" /> NW data came from Agence France-Presse, Xinhua News and Assabah. WT data came from different Google and Yahoo groups, such as IslamToday or YaMuslim. BC data came from different shows on AlJazeera and LBC. BN data came from AlJazeera, LBC and Dubai TV. Our annotator was a female Levantine Arabic speaker. The data overall was rather free of some of the dialectal phenomena that influenced many of our decisions (particularly dialectal orthographic inconsistencies): the transcripts of the BN and BC data were carefully produced according to LDC guidelines (Maamouri et al. 2004b); and the religious theme of the WT data made it more MSA-like than some other web texts we have seen which are much more dialectal. Thus, this set of texts is not truly representative of the types of dialect switching we expect to find.

Table 1 presents various statistics over the annotated set. The first four columns of data belong to the four genres annotated. The next two combine the two transcribed spoken genres (BN and BC) into BX, and the two written genres (WT and NW) into TX. The last column combines all the data. The first data row shows the number of documents. The second shows the number of segments. BX data had more segments (lines) that were shorter (average 8 words) than TX data (average 19). The next five rows show the distribution of segment level labels. Overall BX data has a larger number of segments in level 1 than level 0. This stands in stark contrast to the TX data whose segments are almost all in level 0. Within BX, BC exhibits a higher degree of level 1 than BN. This is a consistent trend with expectations about the genres: BC is less rehearsed and is more reactive (and thus more dialectal) as opposed to BN which is primarily read. Within TX data, we do not see the trend we expect; namely that WT data is more dialectal. This is perhaps a result of the data being of a higher MSA quality than is commonly the case in other news groups because the groups' themes are religious.

Next in Table 1 is a row showing the number of words per genre. This is followed by the distribution of the four word levels. The distributions here are consistent with what we expect: BC is more dialectal than BN than WT than NW. The jump in NW at level 3 is the result of the Levantine annotator considering the spelling of some month names as dialectal because they are different from her MSA: e.g., the name used for the month 'February' was فبراير fibrAyir,

which is acceptable in Egypt as MSA but not in the Levant (corresponding MSA month name is شباط šbAt) (Omar 1976). We will revisit this issue when we discuss inter-annotator agreement in Section 3.2. Overall, at the word annotation, BX has less of level 0 and more of higher levels than TX.

<table><caption>Table 1 Statistics of annotated corpus (levels are percentages). BC is transcribed broadcast conversation, BN is transcribed broadcast news, WT is web texts (web discussion forums), and NW is newswire. BX is the combination of the two transcribed speech genres (BC and BN), while TX is a combination of the two text genres (WT and NW).</caption><thead><tr><th></th><th>BC</th><th>BN</th><th>WT</th><th>NW</th><th>BX</th><th>TX</th><th>ALL</th></tr></thead><tbody><tr><td>Doc's</td><td>6</td><td>8</td><td>17</td><td>28</td><td>14</td><td>45</td><td>59</td></tr><tr><td>Seg's</td><td>640</td><td>437</td><td>280</td><td>287</td><td>1077</td><td>567</td><td>1644</td></tr><tr><td>Level 0</td><td>27.7</td><td>57.4</td><td>98.9</td><td>96.2</td><td>39.7</td><td>97.5</td><td>59.7</td></tr><tr><td>Level 1</td><td>67.5</td><td>41.2</td><td>0.7</td><td>2.4</td><td>56.8</td><td>1.6</td><td>37.8</td></tr><tr><td>Level 2</td><td>3.1</td><td>0.5</td><td>0.4</td><td>1.4</td><td>2.0</td><td>0.9</td><td>1.6</td></tr><tr><td>Level 3</td><td>0.5</td><td>0.2</td><td>0.0</td><td>0.0</td><td>0.4</td><td>0.0</td><td>0.2</td></tr><tr><td>Level 4</td><td>1.3</td><td>0.7</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.7</td></tr><tr><td>Words</td><td>4619</td><td>3919</td><td>5042</td><td>5580</td><td>8538</td><td>10622</td><td>19160</td></tr><tr><td>Level 0</td><td>96.0</td><td>97.9</td><td>98.7</td><td>99.3</td><td>96.9</td><td>99.0</td><td>98.1</td></tr><tr><td>Level 1</td><td>2.2</td><td>1.5</td><td>1.2</td><td>0.2</td><td>1.9</td><td>0.7</td><td>1.2</td></tr><tr><td>Level 2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Level 3</td><td>1.7</td><td>0.6</td><td>0.1</td><td>0.5</td><td>1.2</td><td>0.3</td><td>0.7</td></tr></tbody></table>

## 3.2 Inter-annotator Agreement

We double annotated around 20% of the data (11 documents) roughly equally distributed over all genres. Our second annotator is a female Egyptian Arabic speaker. We consider here word-level and segment-level annotations only. At the word level, the overall agreement (as accuracy) is over 98.5%. The most common label agreed on is level 0 (97.1%). The corresponding Cohen's (1960) kappa score is 0.72 indicating a good degree of agreement. The largest disagreement is between levels 0 and 1. A large portion of the disagreement is the result of our guidelines' lack of specification of MSA standard spelling of some words, particularly proper names; although MSA is standard across the Arab world, some regional varieties of spellings exist. Our Egyptian and Levantine annotators disagreed on whether kuwbinhAjin 'Copenhagen' is level 0 or 1: it is level 0 in Egyptian MSA, but the preferred spelling in Levantine MSA is kuwbinhAjin. Similarly, the spelling of names of continents such as 'Africa' using a Taa Marbuta (افريقيّة Afriqiyqah) was not accepted by the Egyptian annotator who preferred (افريقيّة AfriqiyqA). There is a small portion of levels 0 and 3 disagreement too. These were primarily the result of interpreting interjections literally (as MSA) or functionally (as dialect). For example, the use of waAll-ah 'lit. by God)' as a semantically empty interjection led one of our annotators to mark it as lexically dialectal (level 3). The segment-level annotation inter-annotator agreement is lower than word-level annotation: basic accuracy agreement is 78% and the kappa measure is 0.56.

<footnote n="3">³ http://www.ldc.upenn.edu/</footnote>
## 4. Conclusions and Future Work

We have presented a proposal for annotation guidelines identifying dialect switching between MSA and at least one dialect in written text. We have reported on some initial annotation experiments showing that dialect annotation has distinct distributions in different genres. Our initial results on inter-annotator agreement are encouraging. However, much more work is needed in clarifying and detailing the guidelines. In particular, the results of the inter-annotator agreement analysis suggest a need to address the existing variations of MSA in different regions in the guidelines to specify a reference point and/or make the annotators aware of these variations: we cannot have an annotator flag something as dialectal though it is perfectly acceptable MSA in another part of the Arabic-speaking world. Once the guidelines have been updated, we plan to annotate additional data of different variety in the future.

## 5. Acknowledgements

This work was partially funded by DARPA GALE contract HR0011-06-C-0023 and by NSF SGER grant BCS-0749062. We would like to thank the participants in the April 2008 workshop on the annotation of code switching for their useful feedback.

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