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Named Entity Transliteration with Comparable Corpora

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					   Named Entity
 Transliteration with
 Comparable Corpora
Richard Sproat
Tao Tao
ChengXiang Zhai
                  Coling-ACL 2006
Background
 Names referring to the same person or
  location are likely to co-occur in
  comparable texts.
 Multilingual NE identification is one of the
  important tasks to deal with multilingual
  texts.
 This paper focuses on finding NE
  transliteration pairs appearing in
  comparable corpora.
Overview
   Preprocessing
       English NE recognition
       Chinese transliterated candidates extraction
   Scoring features:
       Pronunciation
       Frequency
   Advanced steps:
       Score propagation
Preprocessing
   English NE recognition
       The NE recognizer described in (Li et al., 2004),
        which is based on the SNoW machine learning
        toolkit (Carlson et al., 1999) is used.
   Chinese candidate extraction
       495 transliterating characters is used.
       A sequence of three or more characters is
        taken as a possible name.
       If the character “‧” occurs, at least one
        character to the left and right of this character
        will be collected, even if the character is not in
        the list of transliterating characters.
Scoring by Pronunciation
   A source-channel model is adopted.
   P(e|c) = P(e’|c’) = ∏iP(ei’|ci’)
       ci’ is fixed to be syllables.
       ei’ ranges over all possible subsequences of the English phone string.
       Pronunciations for English words are obtained using the Festival text-
        to-speech system (Taylor et al., 1998).
   Training
       A small list of 721 names in Roman script and their Chinese equivalent.
       English-Chinese pairs are aligned using the alignment algorithm from
        (Kruskal, 1999), and a hand-derived set of 21 rules-of-thumb.
       Good-Turing estimation is used to estimate probabilities for unseen
        correspondences.
       To filter implausible transliteration pairs:
            For an English phone span to correspond to a Chinese syllable, the initial
             phone of the English span must have been seen in the training data as
             corresponding to the initial of the Chinese syllable some minimum number
             of times.
            4 for consonant-initial syllables.
Scoring by Frequency
   Names of the same entity in different languages often
    have correlated frequency patterns due to common
    triggers such as a major event.
   Scoring Steps:
    1.   Pooling all documents in a single day to form a large pseudo-
         document.
    2.   For each transliteration candidate (both Chinese and English),
         computing its frequency in each pseudo-document and
         obtaining a raw frequency vector.
    3.   Normalizing the raw frequency vector so that it becomes a
         frequency distribution over all the time points (days).
    4.   The Pearson correlation coefficient, cosine (Salton and McGill,
         1983), and Jensen-Shannon divergence (Lin, 1991) are
         used to compute the similarity between two distribution
         vectors.
            Pearson performs best.
Score Propagation
   Document pairs that contain lots of plausible transliteration
    pairs should be viewed as more plausible document pairs.
   In such a situation we should also trust the putative
    transliteration pairs more.
   A co-occurrence relation graph could be built that an edge
    between (ei, ci, wi) and (ej , cj ,wj) is constructed iff (ei, ci)
    and (ej , cj ) co-occur in a certain document pair (Et,Ct).


   P(j|i) is estimated in two different ways:
   The number of co-occurrences in the whole collection.

   A mutual information-based method.
Evaluation
   One day’s worth of comparable news articles (234 Chinese stories
    and 322 English stories) from the Xinhua News agency is taken as
    test corpus.
   About 600 English names were extracted.
        A small number of English names do not seem to have any standard
         transliteration according to the resources that the authors consulted.
        This paper ended up with a list of about 490 out of the 600 English
         names judged.
   627 Chinese candidates were generated.
   The accuracy of the ranked list is measured by Mean Reciprocal
    Rank (MRR).
   Some answers (about 20%) are not in Chinese candidate set due
    to:
    1.   The answer does not occur in the Chinese news articles we look at.
    2.   The answer is there, but the candidate generation method has missed
         it.
   The MRR for the subset of English names whose transliteration
    answers are in the candidate list also compute is also computed.
Evaluation



   Two strategies are experimented for
    combining the two methods:
    1.   Using the phonetic model to filter out (clearly
         impossible) candidates and then use the
         frequency correlation method to rank the
         candidates.
    2.   Averaging the scores of these two methods.
Error Analysis
   To further understand the upper bound of
    our method, we manually add the missing
    correct answers to our candidate set and
    apply all the methods to rank this
    augmented set of candidates.
Experiments on score propagation

				
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