This article is the fourth in a series of \u201cSearch Term Translation for eDiscovery\u201d blogs by Robert Wagner.<\/p>\n
Understanding Code-Switching and Bidirectional Text<\/p>\n
Decoding how two script directions collide in keyword searches<\/p>\n
eDiscovery presents no shortage of complex and time-consuming challenges. This post covers the hardest of them all: working with Hebrew and Arabic search terms. Simply put, it’s a nightmare. The combination of code-switched bidirectional text paired with hidden control characters turns seemingly simple keyword searches into a minefield of frustration and errors. I won\u2019t use the word dread, but Hebrew and Arabic terms are right at the very top of my list of technical and personal nemeses. Let\u2019s dive into why.<\/p>\n
When I talk about code-switching and bidirectional text, those mean very particular things. In linguistics, code-switching refers to alternating between two or more languages within a single sentence. Bidirectional text refers to strings that contain both left-to-right (LTR) and right-to-left (RTL) elements within the same sequence.<\/p>\n
Taken together, what I\u2019m zeroing in on here is the natural tension between keywords pulling RTL and the search operators pulling LTR. Put simply, they don\u2019t sit naturally together, and all sorts of behind-the-scenes tech is attempting to fake harmony in this unhappy marriage of scripts.<\/p>\n
There are a few things going on. For one, as linguists shift back and forth between Arabic keywords and English search operators, they often insert hundreds or thousands of invisible Unicode control characters (invisible LTR and RTL markers) to maintain the correct positioning. But those same characters can scramble searches, break tokenization, or render text in all sorts of unpredictable ways across platforms (e.g., MS office, text editors, and Chrome may all interpret these characters differently). What looks visually correct in Excel may copy-paste in a completely different semantic order\u2014and that difference won\u2019t be obvious to an English speaker.<\/p>\n
Example: Two Arabic words, which is the correct order? \u062a\u0631\u0627\u0646\u0633\u0628\u064a\u0631\u0641\u0643\u062a \u062f\u0628\u064a \/\/\/ \u062f\u0628\u064a \u062a\u0631\u0627\u0646\u0633\u0628\u064a\u0631\u0641\u0643\u062a).<\/p>\n
Control characters is where the chaos begins, but certainly not where it ends.<\/p>\n
The BiDi Algorithm<\/p>\n
Exposing how Unicode’s logic engine misinterprets your search intent<\/p>\n
Another seriously complicating dimension with Arabic and Hebrew search is the Unicode Bidirectional (BiDi) text algorithm. This algorithm, which is integrated into Microsoft Office and many other software applications, classifies the direction of bidirectional text and specifies its visual layout on screen.<\/p>\n
Text classification is as follows:<\/p>\n
\tStrong characters: with inherent directionality (e.g., English [LTR], Arabic [RTL]).
\n\tWeak characters: digits, punctuation like * and brackets — directionality depends on context.
\n\tNeutral characters: whitespace, line breaks, etc.<\/p>\n
The designated weak characters are one of the sources of the menace. The BiDi algorithm then applies cues from surrounding words, with these weak characters ultimately being reordered based on nearby strong characters. Because the BiDi algorithm makes assumptions about what the user intends, it often misinterprets structure\u2014especially when numbers and search operators like wildcards are involved.<\/p>\n
The issue is further amplified in search contexts because strong Arabic characters (keywords) and strong English characters (operators) are often close in length. This parity makes it even harder for the BiDi algorithm to form accurate assumptions.<\/p>\n
Example: (*\u0632\u0648\u062c OR *\u0642\u0631\u064a\u0646 OR *\u0645\u0631\u0627\u0641\u0642 OR *\u0639\u0627\u0626\u0644)\u20147 weak and 21 strong characters. Any guesses how many hidden control characters are lurking?<\/p>\n
Quick Tip: Paste strings into Notepad++ with \u201cShow All Characters\u201d enabled to see the hidden LRM\/RLM\/ALM characters.<\/p>\n
The Specific Failures<\/p>\n
Identifying the four ways BiDi corrupts your search logic<\/p>\n
So how does the BiDi algorithm specifically break Hebrew and Arabic terms?<\/p>\n
\tWildcards shift to the front (right end) of Arabic and Hebrew words
\n\tParts of multi-word Arabic and Hebrew terms leapfrog the OR\/AND search operators and join other keywords (good luck spotting that)
\n\tParentheses can flip direction, becoming )like this( instead of (this)
\n\tNumbers visually reverse order<\/p>\n
We\u2019ve completed hundreds of Arabic and Hebrew term translations over the years, and these issues have been present to some degree in every single one. For added fun, an English speaker armed with a left-to-right keyboard and good intentions will invariably make things worse. They won\u2019t see the invisible control characters and will end up deleting or moving them around. Once one is missing or shifted, everything else in the string can break. At that point, the poor hero is left staring helplessly at the ruins of the string, invariably in great frustration. In search, these two languages are pure joy.<\/p>\n
The Technical Reality Beneath the Surface<\/p>\n
Uncovering why on-screen rendering diverges from what copy-pastes<\/p>\n
Summarizing all the things conspiring to break right-to-left terms:<\/p>\n
\tText is stored in logical order inside files\u2014that is, characters are saved based on the order they were typed, not how they visually appear.
\n\tInvisible control characters, such as U+200E (L\/R Mark) and U+200F (R\/L Mark), are sometimes embedded by linguists, influencing how text is displayed (not how it copy-pastes).
\n\tThe BiDi algorithm interprets the logical text and applies layout rules to render it visually. This rendering is purely for display and can differ from the logical order stored in the file.<\/p>\n
The Key Takeaway: On-screen renderings are not necessarily what copy-pastes. You might see parentheses hugging a Hebrew phrase correctly on screen in MS word, but under the hood, the characters may be stored in a completely different sequence, held in check only by invisible RTL\/LTR override characters. Conversely, a file might store text correctly, but without the right directional control characters, Office displays it as backwards and jumbled\u2014or worse, makes it look correct until a native speaker copy-pastes it and the illusion breaks.<\/p>\n
With bidirectional strings, you\u2019re trusting a silent, invisible choreography of directional rules and rendering engines to get it right. And in many instances, it does not.<\/p>\n
If all of that sounds like you’re living in The Matrix, well, with Arabic and Hebrew search terms, you are living in a matrix of BiDi display bugs.<\/p>\n
Working with Names and Transliteration<\/p>\n
Recognizing why multiple spellings mask the same identity<\/p>\n
I called it a nightmare earlier and stand by the characterization. As someone who specializes in non-English search, I can say this with confidence: with these two languages, it doesn’t get easier the more times you encounter them. Every project brings an invisible slew of code-switch-related problems, and the worst part is that fixing one issue often breaks another elsewhere in the string.<\/p>\n
But forewarned is forearmed. At least now you know what to watch for, even if that knowledge doesn’t make fixing the problems any less painful. It\u2019s also worth noting that if opposing counsel is contending with Hebrew or Arabic data, they are virtually assured of making significant errors unless a native speaker is managing the process.<\/p>\n
I\u2019ll cover translation deficiency analysis reports in a future post. Let\u2019s pivot and talk about names and entities.<\/p>\n
Proper Names: Reverse Transliteration (Not Translation).<\/p>\n
Working with Arabic names as search terms in eDiscovery isn\u2019t really a translation task; it\u2019s transliteration. More specifically, back-transliterating English characters into Arabic is essentially an exercise in reconstructing identity. When you see a name written in English, you\u2019re often looking at one of many possible spellings that all trace back to a single Arabic original.<\/p>\n
Example: \u201cMohamed,\u201d \u201cMuhammad,\u201d \u201cMohamad,\u201d \u201cMehmet,\u201d and 20 others all transliterate back to \u0645\u062d\u0645\u062f.<\/p>\n
The tricky part isn\u2019t back-transliterating English to Arabic; it\u2019s realizing how many different ways that same identity can surface in a dataset, and how many instances you might be missing. This brings us to a subtler point here related to review teams working with English data. While many reviewers can easily associate \u201cMohamed\u201d with \u201cMuhammad,\u201d the connection to \u201cMehmet\u201d may be far less obvious.<\/p>\n
The challenge deepens with less common Arabic names like \u201cIsma\u2019il,\u201d \u201cIsmaeel,\u201d and \u201cEsmaeel,\u201d where transliteration differences can obscure the fact that all three refer to the same person. This is a high-risk area for search that requires careful attention on the English side, particularly for Western review teams relying primarily on machine translation. In many cases, it may not be obvious that the same individual is being referred across documents.<\/p>\n
Construct Forms: The Overlooked Grammatical Challenge<\/p>\n
Understanding how grammatical possession creates search blind spots<\/p>\n
Let\u2019s talk about one final, not-so-obvious nuance of Hebrew and Arabic that trips up many translated term lists: construct forms. Specifically, they are often overlooked and omitted by linguists.<\/p>\n
Construct Forms: Background.<\/p>\n
Construct forms are a bit of an oddity, with no direct parallel in the English language. They are how possession and noun relationships are expressed. In English, possession is typically marked using the word of or apostrophes (\u2019s).<\/p>\n
English Examples:<\/p>\n
\tthe color of the car
\n\tthe car\u2019s color
\n\tPeter\u2019s bonus<\/p>\n
Even though the ending changes with the apostrophe s, the apostrophe is treated as whitespace by search indexes, so \u201ccar\u201d will capture both forms. With construct forms, however, the actual ending of the noun itself changes.<\/p>\n
This distinction has major implications for how nouns need to be translated for search terms. As I\u2019ve mentioned before, most translators\u2014even those highly skilled in language\u2014don\u2019t typically think in terms of search indexes or an eDiscovery mindset. Their focus is linguistic accuracy, not the mechanical behavior of how language elements are tokenized, indexed, and retrieved in a database.<\/p>\n
This is where many linguists unfamiliar with eDiscovery stumble: they don\u2019t adjust for how Arabic and Hebrew grammatical dependencies interact with the indexing logic. In traditional translation work, these relationships are understood conceptually; in eDiscovery term translation, they must be handled operationally through additional search operators and restructured terms.<\/p>\n
Superficially, translating \u201cstock option*\u201d into Hebrew or Arabic seems straightforward. The base translation is the obvious starting point, but effective multilingual searching requires more. You need to include both the root term and construct forms to capture possessive Hebrew and Arabic text such as \u201cJohn\u2019s stock options vest in 2028.\u201d<\/p>\n
Failing to make these adjustments means searches won\u2019t capture all relevant variations, creating blind spots in review and missing key documents. For Hebrew and Arabic datasets, this isn\u2019t a minor oversight\u2014it\u2019s a systemic risk to the matter.<\/p>\n
Conclusion<\/p>\n
Accepting why native speakers are non-negotiable for accuracy<\/p>\n
\u2026nigeb I sa gnidnE (just kidding\u2014but that\u2019s the reality of what can transpire).<\/p>\n
Ending as I begin (because it\u2019s therapeutic), I want to reiterate how much these two languages drive my team nuts due to all the technical complexities noted above. The bottom line is this: Unless Arabic and Hebrew terms are imported into a database and corrected by a native speaker, there’s an extremely high likelihood that results will be at least partially (and potentially wholly) inaccurate.<\/p>\n
But even getting to the point where terms are imported into a database is itself a fraught, painful process, littered with visible and invisible challenges that trip up the majority of people navigating the BiDi path.<\/p>\n
Next Article: The strategic dimension\u2014and hidden leverage\u2014multilingual keyword strategy confers. Most teams treat translation as a checkbox. The sharp ones use it as a weapon. I\u2019ll cover how actively managing this point can massively amplify wider case strategy\u2014or, by equal measure, the damage it can inflict when mishandled.<\/p>\n","protected":false},"excerpt":{"rendered":"This article is the fourth in a series of \u201cSearch Term Translation for eDiscovery\u201d blogs by Robert Wagner.…\n","protected":false},"author":2,"featured_media":395663,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[49,48,61],"class_list":["post-395662","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-ca","tag-canada","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/395662","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=395662"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/395662\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/395663"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=395662"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=395662"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=395662"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}