The problem with standard tokenizers

说明与用途

标准分词器无法处理罗马乌尔都语的非标准拼写和混合语言。构建了一个 DFA 分词器，进行归一化、分词和可视化，达到每秒 10,000 个分词和 92% 噪声降低。

问题

Standard NLP tokenizers fail on Roman Urdu text due to inconsistent spelling, code-mixing, and noise.

结果

The DFA tokenizer achieved 10K tokens/sec, 60 FPS visualization, 92% noise reduction, and complete grammar coverage on test inputs.

工作流

• Normalize input by collapsing repeated letters and mapping phonetic variants to canonical forms.
• Execute hand‑written DFA transition table to classify substrings into token types.
• Visualize DFA execution on a Turing tape and explain tokens via Gemini API.

证据

Repeated letters get collapsed (“kyunnn” becomes “kyun”). This is the single highest-impact rule for Roman Urdu social text.

来源

On my test corpus, the pipeline hit 10K tokens per second parsing throughput, the visualizer held 60 FPS during state transitions, grammar coverage across the defined token set was complete, and the normalization stage reduced surface noise by 92% measured against the raw input.

来源