Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 1073–1082, Sofia, Bulgaria, August 4-9 2013. c⃝2013 Association for Computational Linguistics Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition Mengqiu Wang Stanford University Stanford, CA 94305 mengqiu@cs.stanford.edu Wanxiang Che Harbin Institute of Technology Harbin, China, 150001 car@ir.hit.edu.cn Christopher D. Manning Stanford University Stanford, CA 94305 manning@cs.stanford.edu Abstract Translated bi-texts contain complemen- tary language cues, and previous work on Named Entity Recognition (NER) has demonstrated improvements in perfor- mance over monolingual taggers by pro- moting agreement of tagging decisions be- tween 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 mis- takes, and present a graphical model that performs bilingual NER tagging jointly with word alignment, by combining two monolingual tagging models with two uni- directional alignment models. We intro- duce additional cross-lingual edge factors that encourage agreements between tag- ging and alignment decisions. We design a dual decomposition inference algorithm to perform joint decoding over the com- bined alignment and NER output space. Experiments on the OntoNotes dataset demonstrate that our method yields signif- icant improvements in both NER and word alignment over state-of-the-art monolin- gual baselines. 1 Introduction We study the problem of Named Entity Recogni- tion (NER) in a bilingual context, where the goal is to annotate parallel bi-texts with named entity tags. This is a particularly important problem for machine translation (MT) since entities such as person names, locations, organizations, etc. carry much of the information expressed in the source sentence. Recognizing them provides useful in- formation for phrase detection and word sense dis- ambiguation (e.g., “melody” as in a female name has a different translation from the word “melody” in a musical sense), and can be directly leveraged to improve translation quality (Babych and Hart- ley, 2003). We can also automatically construct a named entity translation lexicon by annotating and extracting entities from bi-texts, and use it to im- prove MT performance (Huang and Vogel, 2002; Al-Onaizan and Knight, 2002). Previous work such as Burkett et al. (2010b), Li et al. (2012) and Kim et al. (2012) have also demonstrated that bi- texts annotated with NER tags can provide useful additional training sources for improving the per- formance of standalone monolingual taggers. Because human translation in general preserves semantic equivalence, bi-texts represent two per- spectives on the same semantic content (Burkett et al., 2010b). As a result, we can find complemen- tary cues in the two languages that help to dis- ambiguate named entity mentions (Brown et al., 1991). For example, the English word “Jordan” can be either a last name or a country. Without sufficient context it can be difficult to distinguish the two; however, in Chinese, these two senses are disambiguated: “乔丹” as a last name, and “约旦” as a country name. In this work, we first develop a bilingual NER model …

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