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    <titleInfo>
        <title>Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining</title>
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    <name type="personal">
        <namePart type="given">Hassan</namePart>
        <namePart type="family">Sajjad</namePart>
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        <namePart type="given">Helmut</namePart>
        <namePart type="family">Schmid</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Fraser</namePart>
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    <name type="personal">
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        <namePart type="family">Schütze</namePart>
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    <abstract>We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the transliteration sub-model learns to generate transliteration pairs and the fixed non-transliteration model generates the noise pairs. After training, the unlabeled data is disambiguated based on the posterior probabilities of the two sub-models. We evaluate our transliteration mining system on data from a transliteration mining shared task and on parallel corpora. For three out of four language pairs, our system outperforms all semi-supervised and supervised systems that participated in the NEWS 2010 shared task. On word pairs extracted from parallel corpora with fewer than 2% transliteration pairs, our system achieves up to 86.7% F-measure with 77.9% precision and 97.8% recall.</abstract>
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    <identifier type="doi">10.1162/COLI_a_00286</identifier>
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        <date>2017-06</date>
        <detail type="volume"><number>43</number></detail>
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            <start>349</start>
            <end>375</end>
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%0 Journal Article
%T Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining
%A Sajjad, Hassan
%A Schmid, Helmut
%A Fraser, Alexander
%A Schütze, Hinrich
%J Computational Linguistics
%D 2017
%8 June
%V 43
%N 2
%I MIT Press
%C Cambridge, MA
%F sajjad-etal-2017-statistical
%X We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the transliteration sub-model learns to generate transliteration pairs and the fixed non-transliteration model generates the noise pairs. After training, the unlabeled data is disambiguated based on the posterior probabilities of the two sub-models. We evaluate our transliteration mining system on data from a transliteration mining shared task and on parallel corpora. For three out of four language pairs, our system outperforms all semi-supervised and supervised systems that participated in the NEWS 2010 shared task. On word pairs extracted from parallel corpora with fewer than 2% transliteration pairs, our system achieves up to 86.7% F-measure with 77.9% precision and 97.8% recall.
%R 10.1162/COLI_a_00286
%U https://aclanthology.org/J17-2003
%U https://doi.org/10.1162/COLI_a_00286
%P 349-375

Markdown (Informal)

[Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining](https://aclanthology.org/J17-2003) (Sajjad et al., CL 2017)

• Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining (Sajjad et al., CL 2017)

ACL

• Hassan Sajjad, Helmut Schmid, Alexander Fraser, and Hinrich Schütze. 2017. Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining. Computational Linguistics, 43(2):349–375.