md/UPDATES_AND_FIXES.md

Recent Updates and Fixes

Overview

Two important fixes have been implemented based on testing feedback:

1. Leetspeak Translation (before NER)
2. Improved Country Mapping (check ALL tags)

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Fix 1: Leetspeak Translation

Problem

Names with leetspeak (numbers replacing letters) weren't being properly cleaned:

• 4kira should be Akira
• 1rene should be Irene
• 3mma should be Emma

Solution

Added leetspeak translation before other NER processing in Cell 5.

Mapping Table

Leetspeak	Letter
4	A
3	E
1	I
0	O
7	T
5	S
8	B
9	G
@	A
$	S
!	I

Examples

4kira     -> akira
3mma      -> emma
1rene     -> irene
L3vi      -> Levi
S4sha     -> Sasha
K4te      -> Kate
J3ssica   -> Jessica

Implementation

The translate_leetspeak() function runs FIRST in clean_name(), before emoji removal and other cleaning steps. This ensures leetspeak is converted to proper letters before any other processing.

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Fix 2: Improved Country Mapping

Problem

The country mapping was stopping at the first match, which meant:

• Irene with tags ['girl', 'photorealistic', 'asian', 'woman', 'beautiful', 'celebrity', 'korean']
• The 'korean' tag wasn't being properly mapped to 'South Korea'
• This resulted in incomplete hints being sent to the LLM
• Expected: Deepseek should identify Bae Joo-hyun (Irene) from Red Velvet

Solution

Updated Cell 7 to:

1. Check ALL tags (not just stop at first match)
2. Use a priority system to select the best match:
   • Priority 3: Exact country name match (highest)
   • Priority 2: Nationality match (medium)
   • Priority 1: Word parts (lowest)

How It Works

Before (Broken)

def infer_country_and_nationality(tags):
    for tag in tags:
        if tag in mapping:
            return mapping[tag]  # ❌ Stops at first match!
    return ("", "")

After (Fixed)

def infer_country_and_nationality(tags):
    best_match = None
    best_priority = 0

    for tag in tags:  # ✅ Check ALL tags
        if tag in mapping:
            country, nationality, priority = mapping[tag]
            if priority > best_priority:
                best_match = (country, nationality)
                best_priority = priority

    return best_match or ("", "")

Example: Irene Case

Input Tags: ['girl', 'photorealistic', 'asian', 'woman', 'beautiful', 'celebrity', 'korean']

Processing:

1. Check 'girl' → no match
2. Check 'photorealistic' → no match
3. Check 'asian' → no match (too generic)
4. Check 'woman' → no match
5. Check 'beautiful' → no match
6. Check 'celebrity' → no match
7. Check 'korean' → ✅ MATCH!
   • Maps to nationality: 'South Korean'
   • Which maps to country: 'South Korea'
   • Priority: 2 (nationality match)

Output:

• likely_country: 'South Korea'
• likely_nationality: 'South Korean'

Sent to Deepseek:

Given 'Irene' (celebrity, South Korea), provide:
1. Full legal name
2. Aliases
3. Gender
4. Top 3 professions
5. Country

Expected Result: Deepseek can now identify this as Bae Joo-hyun (Irene), a South Korean singer/actress from the K-pop group Red Velvet.

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Impact on Results

Better Name Recognition

• Leetspeak names are now properly translated
• LLMs receive cleaner, more recognizable names

Better Country Context

• All tags are now considered for country mapping
• More accurate country/nationality hints sent to LLMs
• Better identification of international celebrities

Example Improvements

Name	Tags	Before	After
4kira LoRA	['japanese', 'actress']	'4kira' + no country	'Akira' + 'Japan'
Irene	['korean', 'celebrity']	'Irene' + no country	'Irene' + 'South Korea'
1U	['korean', 'singer']	'1U' + no country	'IU' + 'South Korea'
3lsa	['model']	'3lsa' + no country	'Elsa' + country if tagged

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Testing Recommendations

Before Running Full Pipeline

1. Test Leetspeak Translation (Cell 5):

   # Look for names with numbers in the output
   # Verify they're properly translated
   

2. Test Country Mapping (Cell 7):

   # Check the debug output at the end:
   # "🔍 Checking 'Irene' entries:"
   # Verify country is properly mapped
   

3. Test Deepseek Results (Cell 10):

   # Look for Irene in the results
   # Should now identify as Bae Joo-hyun
   

Validation Checklist

• Leetspeak names are translated (check console output in Cell 5)
• Country mapping shows high success rate (check stats in Cell 7)
• Irene is correctly identified as Bae Joo-hyun (check results in Cell 10)
• Other K-pop/Korean celebrities are properly identified
• Japanese/Chinese celebrities also benefit from better country mapping

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Notes

Why Check ALL Tags?

Some entries have many tags, and the most informative tag might not be first:

tags = ['girl', 'sexy', 'beautiful', 'asian', 'korean', 'celebrity', 'kpop']
                                                  ^^^^ Most informative!

The old code might stop at 'girl' or 'asian' (no country info), missing the 'korean' tag.

Why Use Priority?

Some tags might match multiple countries. Priority ensures we get the best match:

• 'american' → exact nationality match (priority 2) → USA
• 'america' → could be North/South/Central America (priority 1)

The system picks the higher priority match.

Word Length Filter

Word parts only match if >4 characters to avoid false positives:

• ✅ 'china' → matches China (5 chars)
• ❌ 'us' → too short, might be part of other words

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Future Improvements

Potential enhancements:

1. More leetspeak patterns: |\/| for M, (_) for U, etc.
2. Fuzzy country matching: Handle typos like 'corean' → 'korean'
3. Multi-country support: Some celebrities work in multiple countries
4. Language detection: Use name structure to infer origin

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Summary

✅ Leetspeak translation ensures names are readable before NER ✅ ALL tags checked ensures no country hints are missed ✅ Priority system ensures best match is selected ✅ Better LLM results from improved name quality and country context

These fixes should significantly improve the accuracy of person identification, especially for:

• International celebrities (K-pop, J-pop, C-pop)
• Names with leetspeak
• Entries where country info appears later in tag list