Evaluating Persian Tokenizers

TL;DR

This paper evaluates various Persian tokenizers, introducing a comparison framework that highlights the hybrid Farsi Verb and Hazm tokenizer with bounded morphemes fixing as the most effective, achieving an F1 score of 98.97%.

Contribution

It introduces a comprehensive evaluation of Persian tokenizers and identifies the most effective hybrid approach for accurate tokenization.

Findings

Hybrid Farsi Verb and Hazm tokenizer with bounded morphemes fixing performs best.

Achieved an F1 score of 98.97% on Persian texts.

Evaluation framework can be used for future tokenizer assessments.

Abstract

Tokenization plays a significant role in the process of lexical analysis. Tokens become the input for other natural language processing tasks, like semantic parsing and language modeling. Natural Language Processing in Persian is challenging due to Persian's exceptional cases, such as half-spaces. Thus, it is crucial to have a precise tokenizer for Persian. This article provides a novel work by introducing the most widely used tokenizers for Persian and comparing and evaluating their performance on Persian texts using a simple algorithm with a pre-tagged Persian dependency dataset. After evaluating tokenizers with the F1-Score, the hybrid version of the Farsi Verb and Hazm with bounded morphemes fixing showed the best performance with an F1 score of 98.97%.