• Corpus ID: 17221043

Language Identification from Text Using N-gram Based Cumulative Frequency Addition

@inproceedings{Ahmed2004LanguageIF,
  title={Language Identification from Text Using N-gram Based Cumulative Frequency Addition},
  author={Bashir Ahmed and Sung-Hyuk Cha and Charles C. Tappert},
  year={2004}
}
This paper describes the preliminary results of an efficient language classifier using an ad-hoc Cumulative Frequency Addition of N-grams. The new classification technique is simpler than the conventional Naive Bayesian classification method, but it performs similarly in speed overall and better in accuracy on short input strings. The classifier is also 5-10 times faster than N-gram based rank-order statistical classifiers. Language classification using N-gram based rank-order statistics has… 

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References

SHOWING 1-10 OF 15 REFERENCES

N-gram-based text categorization

An N-gram-based approach to text categorization that is tolerant of textual errors is described, which worked very well for language classification and worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject.

High-quality text-to-speech synthesis : an overview

This paper tries to give a comprehensive introduction to state-of-the-art Text-ToSpeech (TTS) synthesis by highlighting its Digital Signal Processing (DSP) and Natural Language Processing (NLP)

Multilingual text analysis for text-to-speech synthesis

  • R. Sproat
  • Linguistics
    Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96
  • 1996
We present a model of text analysis for text-to-speech (TTS) synthesis based on weighted finite state transducers, which serves as the text-analysis module of the multilingual Bell Labs TTS system.

Mixed-lingual text analysis for polyglot TTS synthesis

It is shown how an analyzer for mixedlingual texts can be realized for a set of languages, starting from a corresponding set of monolingual analyzers which are based on DCGs and chart parsing.

From multilingual to polyglot speech synthesis

A distinction between existing multilingual synthesis systems and mixed-lingual or polyglot synthesis systems that should be capable of synthesising with the same voice utterances which contain foreign language words or word groups is proposed.

Multilingual Sentence Categorization according to Language

An approach to sentence categorization which has the originality to be based on natural properties of languages with no training set dependency is described, which is fast, small, robust and textual errors tolerant.

Statistical Identification of Languages

  • Statistical Identification of Languages
  • 1994

N-gram based Text Categorization, Symposium on Document Analysis and Information Retrieval

  • N-gram based Text Categorization, Symposium on Document Analysis and Information Retrieval
  • 1994

The Stakes of Multilinguality: Multilingual Text Tokenization in Natural Language Diagnosis

  • Proceedings of the 4rth International Conference on Artificial Inteligence Workshop
  • 1996