On Jointly Recognizing and Aligning Bilingual Named Entities

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 631–639, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics On Jointly Recognizing and Aligning Bilingual Named Entities Yufeng Chen, Chengqing Zong Institute of Automation, Chinese Academy of Sciences Beijing, China {chenyf,cqzong}@nlpr.ia.ac.cn Keh-Yih Su Behavior Design Corporation Hsinchu, Taiwan, R.O.C. bdc.kysu@gmail.com Abstract We observe that (1) how a given named en- tity (NE) is translated (i.e., either semanti- cally or phonetically) depends greatly on its associated entity type, and (2) entities within an aligned pair should share the same type. Also, (3) those initially detected NEs are an- chors, whose information should be used to give certainty scores when selecting candi- dates. From this basis, an integrated model is thus proposed in this paper to jointly identify and align bilingual named entities between Chinese and English. It adopts a new map- ping type ratio feature (which is the propor- tion of NE internal tokens that are semanti- cally translated), enforces an entity type con- sistency constraint, and utilizes additional monolingual candidate certainty factors (based on those NE anchors). The experi- ments show that this novel approach has sub- stantially raised the type-sensitive F-score of identified NE-pairs from 68.4% to 81.7% (42.1% F-score imperfection reduction) in our Chinese-English NE alignment task. 1 Introduction In trans-lingual language processing tasks, such as machine translation and cross-lingual informa- tion retrieval, named entity (NE) translation is essential. Bilingual NE alignment, which links source NEs and target NEs, is the first step to train the NE translation model. Since NE alignment can only be conducted af- ter its associated NEs have first been identified, the including-rate of the first recognition stage significantly limits the final alignment perform- ance. To alleviate the above error accumulation problem, two strategies have been proposed in the literature. The first strategy (Al-Onaizan and Knight, 2002; Moore, 2003; Feng et al., 2004; Lee et al., 2006) identifies NEs only on the source side and then finds their corresponding NEs on the target side. In this way, it avoids the NE recognition errors which would otherwise be brought into the alignment stage from the target side; however, the NE errors from the source side still remain. To further reduce the errors from the source side, the second strategy (Huang et al., 2003) expands the NE candidate-sets in both languages before conducting the alignment, which is done by treating the original results as anchors, and then re-generating further candidates by enlarg- ing or shrinking those anchors' boundaries. Of course, this strategy will be in vain if the NE an- chor is missed in the initial detection stage. In our data-set, this strategy significantly raises the NE-pair type-insensitive including-rate 1 from 83.9% to 96.1%, and is thus adopted in this paper. Although the above expansion strategy has substantially alleviated the error accumulation problem, the final alignment accuracy is still not good (type-sensitive F-score only 68.4%, as indi- cated in Table 2 in Section 4.2). After having examined the data, we found …

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