Syntactic Text Processing is a subfield of Natural Language Processing (NLP) that deals with the analysis of the syntax and grammatical structure of natural language texts.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Phonology is a subfield of Syntactic Text Processing that studies the sound of languages or dialects and the patterns and rules that govern the use and combination of sounds to form meaningful units. It involves the analysis of phonemes, prosody, and intonation of speech, and the development of models and theories that can explain and predict the behavior and variability of sounds in different contexts.

We investigated the applicability of probabilistic context-free grammars to syllabi cation and grapheme-to-phoneme conversion. The results show that the standard probability model of context-free grammars performs very well in predicting syllable boundaries. However, our results indicate that the standard probability model does not solve grapheme-to-phoneme conversion su ciently although, we varied all free parameters of the probabilistic reestimation procedure.

Publication:

Probabilistic Context-Free Grammars for Syllabification and Grapheme-to-Phoneme Conversion

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