| 预测模型 | 预测结果 | | BiLSTM-CNN-CRF（POS+REF+DIS） | We have adopted the Conditional Maximum Entropy (MaxEnt) modeling paradigm as outlined in REF3 and REF19 | | To quickly (and approximately) evaluate this phenomenon, we trained the statistical IBM word-alignment model 4 REF7, using the GIZA ++ software REF11 for the following language pairs: Chinese-English, Italian-English, and Dutch-English, using the IWSLT-2006 corpus REF23 for the first two language pairs, and the Europarl corpus REF9 for the last one. | | In computational linguistic literature, much effort has been devoted to phonetic transliteration, such as English-Arabic, English-Chinese REF5, English-Japanese REF6 and English-Korean. | | Tokenisation, species word identification and chunking were implemented in-house using the LTXML2 tools REF4, whilst abbreviation extraction used the Schwartz and Hearst abbreviation extractor REF9 and lemmatisation used morpha REF12. |
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