Classifying Graphemes in English Words Through the Application of a
  Fuzzy Inference System
Samuel Rose Chandrasekhar Kambhampati
In Linguistics, a grapheme is a written unit of a writing system
corresponding to a phonological sound. In Natural Language Processing tasks,
written language is analysed through two different mediums, word analysis, and
character analysis. This paper focuses on a third approach, the analysis of
graphemes. Graphemes have advantages over word and character analysis by being
self-contained representations of phonetic sounds. Due to the nature of
splitting a word into graphemes being based on complex, non-binary rules, the
application of fuzzy logic would provide a suitable medium upon which to
predict the number of graphemes in a word. This paper proposes the application
of a Fuzzy Inference System to split words into their graphemes. This Fuzzy
Inference System results in a correct prediction of the number of graphemes in
a word 50.18% of the time, with 93.51% being within a margin of +- 1 from the
correct classification. Given the variety in language, graphemes are tied with
pronunciation and therefore can change depending on a regional accent/dialect,
the +- 1 accuracy represents the impreciseness of grapheme classification when
regional variances are accounted for. To give a baseline of comparison, a
second method involving a recursive IPA mapping exercise using a pronunciation
dictionary was developed to allow for comparisons to be made.