Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition
Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition
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发表时间：
2013-08
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通讯作者：
Mengqiu Wang;Wanxiang Che;Christopher D. Manning
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作者：
Mengqiu Wang;Wanxiang Che;Christopher D. Manning
翻译的双文本包含互补的语言线索，和以前的工作命名实体识别（NER）已经证明，通过促进协议的两种语言之间的标记决定单语标签的性能改善。然而，大多数以前的双语标注方法假设单词对齐是固定的输入，这可能会导致级联错误。我们观察到，NER标签信息可以用来纠正对齐错误，并提出了一个图形化的模型，执行双语NER标记联合字对齐，结合两个单语标记模型与两个单向对齐模型。我们引入了额外的跨语言边缘因素，鼓励标签和对齐决策之间的协议。我们设计了一个对偶分解推理算法，在组合对齐和NER输出空间上执行联合解码。OntoNotes数据集上的实验表明，我们的方法在NER和单词对齐方面都比最先进的单语基线有了显着的改进。
Translated bi-texts contain complementary language cues, and previous work on Named Entity Recognition (NER) has demonstrated improvements in performance over monolingual taggers by promoting agreement of tagging decisions between the two languages. However, most previous approaches to bilingual tagging assume word alignments are given as fixed input, which can cause cascading errors. We observe that NER label information can be used to correct alignment mistakes, and present a graphical model that performs bilingual NER tagging jointly with word alignment, by combining two monolingual tagging models with two unidirectional alignment models. We introduce additional cross-lingual edge factors that encourage agreements between tagging and alignment decisions. We design a dual decomposition inference algorithm to perform joint decoding over the combined alignment and NER output space. Experiments on the OntoNotes dataset demonstrate that our method yields significant improvements in both NER and word alignment over state-of-the-art monolingual baselines.