Predicting the Number of Graphemes in English Words Using a Fuzzy Inference System

Core Concepts

A fuzzy inference system can be used to predict the number of graphemes in English words with reasonable accuracy, accounting for the complex and non-binary nature of grapheme-to-phoneme mapping.

Abstract

This paper presents a method for classifying graphemes in English words using a fuzzy inference system. Graphemes are the smallest functional units of a writing system, corresponding to phonological sounds.
The key highlights and insights are:

Grapheme decoding in English is challenging due to the large number of graphemes compared to phonemes, with multiple graphemes representing each phoneme and the same grapheme representing multiple phonemic sounds depending on context.

The authors analyzed a corpus of the 10,000 most common English words and found that the number of graphemes in a word roughly follows a normal distribution when compared to word length.

Using the mean and standard deviation of the number of characters, vowels, and consonants for words with 1-14 graphemes, the authors developed fuzzy membership functions and a Mamdani fuzzy inference system to predict the number of graphemes in a word.

The fuzzy inference system correctly predicted the number of graphemes 50.18% of the time on the training corpus, with 93.51% of predictions being within a margin of +/- 1 of the correct classification.

The authors also developed a second method using IPA mapping, which resulted in a higher proportion of words being split into the correct number of graphemes, but the fuzzy inference system performed better at mapping the graphemes correctly.

The authors conclude that the fuzzy inference system approach is promising for phonological word structure analysis in NLP and NLG applications, as it can handle the complex and non-binary nature of grapheme-to-phoneme mapping.

Stats

The longest word in the corpus is "telecommunications" with 18 characters.
The 7,000 most common words in English account for approximately 90% of word usage.

Quotes

"Due to the nature of splitting a word into graphemes being based on complex, non-binary rules, the application of fuzzy logic [11] would provide a suitable medium upon which to predict the number of graphemes in a word."
"Because of these challenges in decoding graphemes, the decoding of a word's graphemes does not fit into discrete categories. Due to this, we hypothesise that by applying Fuzzy Set Theory, English words can be computationally decoded into their graphemes by applying fuzzy sets and fuzzy inference systems."

Key Insights Distilled From

Classifying Graphemes in English Words Through the Application of a Fuzzy Inference System

by Samuel Rose,... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01953.pdf

Deeper Inquiries

How could the fuzzy inference system be further improved to increase the accuracy of grapheme prediction, especially for words with more than 12 graphemes?

To enhance the accuracy of grapheme prediction for words with more than 12 graphemes, several improvements can be implemented. One approach could involve refining the fuzzy rules within the Fuzzy Inference System to account for the complexities introduced by longer words. By introducing additional rules that consider the distribution of graphemes in longer words, the system can better handle the intricacies of these cases. Moreover, incorporating more granular membership functions for input features such as word length, vowel count, and consonant count could provide a more detailed representation of the data, leading to more accurate predictions for words with higher grapheme counts. Additionally, expanding the training dataset to include a diverse range of words with varying grapheme lengths can help the system learn and adapt to the nuances of longer words, thereby improving its predictive capabilities in such scenarios.

What other linguistic features, beyond word length, vowel count, and consonant count, could be incorporated into the fuzzy inference system to improve its performance?

Incorporating additional linguistic features beyond word length, vowel count, and consonant count can further enhance the performance of the fuzzy inference system in grapheme prediction. One potential feature to consider is syllable structure, as the number and arrangement of syllables in a word can provide valuable insights into its grapheme composition. By analyzing syllable patterns and incorporating this information into the fuzzy inference system, a more comprehensive understanding of the word's grapheme structure can be achieved. Furthermore, considering phonotactic constraints, which define the permissible sequences of phonemes in a language, can aid in refining the grapheme prediction process. By integrating phonotactic rules into the system, it can better capture the phonological relationships between graphemes in words, leading to more accurate predictions. Additionally, exploring features related to stress patterns, morphological complexity, or orthographic regularities could offer further avenues for improving the system's performance in grapheme classification.

How could the grapheme classification approach developed in this paper be applied to other languages with different writing systems and phonological structures?

The grapheme classification approach outlined in the paper can be adapted and applied to other languages with distinct writing systems and phonological structures by customizing the fuzzy inference system to accommodate the specific characteristics of each language. To extend the approach to different languages, researchers can first conduct a comprehensive analysis of the target language's grapheme inventory, phonetic properties, and orthographic conventions. By identifying the unique graphemes and phonological patterns in the language, tailored membership functions and fuzzy rules can be developed to suit the linguistic nuances of that particular language. Moreover, incorporating language-specific linguistic features, such as tonal distinctions, diacritics, or digraphs, into the fuzzy inference system can improve its accuracy in predicting graphemes for words in the target language. Collaborating with linguists and native speakers proficient in the language can also provide valuable insights for refining the grapheme classification approach and ensuring its effectiveness across diverse linguistic contexts.