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NER ya Lugha Nyingi: Kiingereza Kinashindwa Kiarabu

Mifano ya NER iliyofunzwa kwa Kiingereza inafikia usahihi wa 85-92%. Kiarabu na Kichina? Mara nyingi 50-70%. Jifunza kuhusu changamoto za kiufundi na jinsi ya kujenga mfumo wa kweli.

February 26, 20268 dakika kusoma
NERmultilingualArabic NLPChinese NLPPII detection

NER ya Lugha Nyingi: Changamoto katika Ugunduzi wa PII

Imesasishwa kwa 2026

Pengo la Usahihi

Mifano ya NER iliyofunzwa kwa Kiingereza inafikia F1 ya 85–92% katika majaribio ya kawaida. Tumia mifano hiyo hiyo kwa matini ya Kiarabu au Kichina. Usahihi unashuka hadi 50–70%.

Kwa kazi ya PII, pengo hilo ni tatizo. Kiwango cha 70% cha mafanikio kunamaanisha 30% ya data nyeti haionekani.

Sababu si hitilafu. Zinatoka jinsi mifumo ya uandishi inavyotofautiana.

Sababu Nne za Msingi

1. Mipaka ya Maneno

Kiingereza hugawanya maneno kwa nafasi. Uashiriaji ni rahisi.

Kichina hakina nafasi kabisa.

``` "张伟住在北京" → Gawanya kwanza: ["张伟", "住在", "北京"] ```

Mfano hauwezi kuweka alama kinachokosa kupata. Kugawanya lazima kuje kabla ya NER.

Kiarabu huunganisha herufi ndani ya neno. Vokali fupi zinaachwa. Maandishi yanasomwa kulia kwenda kushoto.

``` "محمد يعيش في دبي" → Hakuna vokali fupi, kulia kwenda kushoto, herufi zilizoungwa ```

2. Mofolojia

Vitenzi vya Kiingereza hubadilika kwa njia chache. Kiarabu hutumia mfumo wa mizizi. Mzizi mmoja unaunda maneno mengi.

``` كتب (k-t-b, "kuandika") → كاتب (mwandishi), كتاب (kitabu), مكتبة (maktaba) ```

NER lazima ichanganue mizizi kupata majina katika maumbo ya maneno yaliyotokana.

3. Mifumo ya Majina

Majina ya Kilatini yanakwenda Kwanza kisha la Mwisho. Majina katika lugha za RTL yanaunganisha viungo vya familia.

``` محمد بن عبد الله (Muhammad mwana wa Abdullah) ```

Majina ya Kichina yanaweka jina la familia kwanza. Majina mengi yana herufi mbili au tatu.

``` 张伟 (Zhang Wei) — herufi 2 欧阳修 (Ouyang Xiu) — herufi 3 ```

Mfano uliojengwa kwa mifumo ya majina ya Magharibi utakosa muundo huu.

4. Mwelekeo wa Maandishi

Baadhi ya lugha zinaendesha kulia kwenda kushoto. Maandishi ya RTL yakishikilia jina la Kiingereza, mpangilio wa kuona na wa kimantiki unajitenga. Hii inaitwa matini ya BiDi. Inahitaji uchambuzi makini.

Alama za F1 kwa Mfumo wa Uandishi

LughaMfumo wa UandishiKipimo cha F1Kiwango
KiingerezaKilatini85–92%Chini
KijerumaniKilatini82–88%Chini
KifaransaKilatini80–87%Chini
KihispaniaKilatini81–86%Chini
KirusiCyrillic75–83%Wastani
KiarabuAbjad55–75%Juu
KichinaHanzi60–78%Juu
KijapaniMchanganyiko65–80%Juu
KithaiThai50–70%Juu Sana
KihindiDevanagari60–75%Juu

Mifumo isiyo ya Kilatini na mapungufu ya maneno yanashuka alama kwa ujumla.

Suluhisho la Viwango Vitatu

Tunatumia viwango vitatu kufunika lugha 48 na mifumo ya uandishi.

Kiwango cha 1: spaCy — Lugha 25

Kwa lugha zenye mifano iliyoimarishwa na kujaribiwa. Inafunika Kiingereza, Kijerumani, Kifaransa, Kihispania, Kiitaliano, Kireno, Kiholanzi, Kipolishi, Kirusi, na Kigiriki.

Kiwango cha 2: Stanza — Lugha Ngumu

Stanford Stanza inashughulikia Kiarabu, Kichina, Kijapani, na Kikorea. Inafanya migawanyiko ya maneno na uchambuzi wa mizizi kabla ya NER.

Kiwango cha 3: XLM-RoBERTa — Lugha Zenye Rasilimali Chache

Kwa lugha ambazo hazina mifano maalum. Kithai, Kivietinamu, Kihindi, Kibengali, Kiebrania, Kituruki, na Kiajemi zinaenda hapa. Inashughulikia matini ya lugha mchanganyiko bila alama wazi inayohitajika.

RTL na BiDi

Matini ya kulia kwenda kushoto inahitaji hatua za ziada zaidi ya kugawanya.

Mfumo wetu:

  1. Unasawazisha matini kwa mpangilio wa kimantiki.
  2. Unaendesha NER kwa mpangilio huo.
  3. Unarejesha nafasi za viumbe kurudi kwa mpangilio wa kuona.

Tunaondoa viambishi vya awali vilivyoambatanishwa kabla ya NER na kuvirudisha baadaye.

``` "محمد" — jina tu "لمحمد" — "kwa Muhammad" (kiambishi cha awali kimewashwa) ```

Kubadilisha Msimbo

Hati halisi mara nyingi zinachanganya lugha katika mstari mmoja.

``` "El meeting con John es at 3pm" "我今天跟John去shopping" ```

Mfumo wetu hugawanya kwa lugha. Unaendesha mfano sahihi kwenye kila sehemu. Kisha unaunganisha matokeo na ramani ya nafasi.

Vipimo vya Ndani

Matokeo kutoka majaribio ya ndani kwenye data ya lugha mchanganyiko:

HaliF1
Kiingereza peke yake91%
Kijerumani peke yake88%
Kiarabu peke yake79%
Kichina peke yake81%
Mchanganyiko wa Kiingereza-Kiarabu83%
Mchanganyiko wa Kiingereza-Kichina84%
Mchanganyiko wa Kiingereza-Kijerumani89%

Maelezo ya Usanidi

Programu ya desktop hugundua lugha kiotomatiki kwa kila hati. Kwa faili za lugha mchanganyiko, inashughulikia kila sehemu na mfano sahihi. Hakuna hatua ya mkono inayohitajika.

Weka lugha katika API unapokuijua:

```json { "text": "محمد بن عبد الله", "language": "ar" } ```

Tumia ugunduzi wa kiotomatiki unapokosa:

```json { "text": "محمد بن عبد الله", "language": "auto" } ```

Mifumo ya kawaida inapaswa kufunika tarakimu mahususi za eneo:

```

Kitambulisho cha mfanyakazi cha Kilatini

EMP-[0-9]{6}

Kitambulisho cha mfanyakazi cha Kiarabu (inajumuisha tarakimu za Kiarabu-Indic)

موظف-[٠-٩0-9]{6} ```

Angalia orodha kamili ya viumbe. Kwa usanidi wa API, tembelea ukurasa wa vipengele vya API. Mwongozo wetu wa uzingatifu wa GDPR unafunika jinsi mapungufu ya ugunduzi yanavyoathiri sheria ya ulinzi wa data.


anonym.legal inatumia mfumo wa NER wa viwango vitatu — spaCy, Stanza, na XLM-RoBERTa — kufunika lugha 48 na ugunduzi thabiti wa PII.

Vyanzo

Tayari kulinda data yako?

Anza kuanonymisha PII na aina 285+ za vitu katika lugha 48.

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

Related reading

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.