• Corpus ID: 3894514

Graph-Based N-gram Language Identication on Short Texts

@inproceedings{Tromp2011GraphBasedNL,
  title={Graph-Based N-gram Language Identication on Short Texts},
  author={Erik Tromp and Mykola Pechenizkiy},
  year={2011}
}
classication, n-gram Abstract Language identication (LI) is an important task in natural language processing. Sev- eral machine learning approaches have been proposed for addressing this problem, but most of them assume relatively long and well written texts. We propose a graph-based N-gram approach for LI called LIGA which targets relatively short and ill-written texts. The results of our experimental study show that LIGA outperforms the state-of-the-art N-gram approach on Twitter messages LI. 

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References

SHOWING 1-10 OF 13 REFERENCES
A Comparative Study on Language Identification Methods
TLDR
This work presents the evaluation results and discusses the importance of a dynamic value for the out-of-place measure and the Ad-Hoc Ranking classification method.
Language Recognition for Mono-and Multi-lingual Documents
TLDR
The monolingual algorithm, which allows for segmenting a multilingual document into single language chunks and identifying the languages of those chunks, significantly outperforms commonly used language recognition algorithms.
N-gram-based text categorization
TLDR
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.
Linguini: language identification for multilingual documents
  • J. Prager
  • Computer Science
    Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers
  • 1999
TLDR
Linguini could identify the language of documents as short as 5-10% of the size of average Web documents with 100% accuracy, and can be applied to subject categorization systems to distinguish between cases where, when the system recommends two or more categories, the document belongs strongly to all or really to none.
Language Identification from Text Using N-gram Based Cumulative Frequency Addition
TLDR
The preliminary results of an efficient language classifier using an ad-hoc Cumulative Frequency Addition of N-grams are described, which is simpler than the conventional Naive Bayesian classification method but performs similarly in speed overall and better in accuracy on short input strings.
Evaluation of Language Identification Methods
TLDR
Three freely available language identification programs are tested and evaluated and explained how they work, and commented on their accuracy.
Using compression based language models for text categorization.
TLDR
Two approaches to compression-based categorization are presented, one based on ranking by documentCross entropy (average bits per coded symbol) with respect to a category model, and the other based on document cross entropy difference between category and complement of category models.
Extension of Zipf's Law to Word and Character N-grams for English and Chinese
TLDR
It is shown that for a large corpus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks, but when single words or characters are combined together with n-gram Words or characters in one list and put in order of frequency, the frequency of tokens in the combined list follows Zipf’s law approximately.
Statistical Identification of Language
Multilingual sentiment analysis on social media. Master's thesis
  • Multilingual sentiment analysis on social media. Master's thesis
  • 2011
...
...