Named Entity Transliteration and Discovery from Multilingual Comparable Corpora

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 82–88, New York, June 2006. c⃝2006 Association for Computational Linguistics Named Entity Transliteration and Discovery from Multilingual Comparable Corpora Alexandre Klementiev Dan Roth Department of Computer Science University of Illinois Urbana, IL 61801 � klementi,danr � @uiuc.edu Abstract Named Entity recognition (NER) is an im- portant part of many natural language pro- cessing tasks. Most current approaches employ machine learning techniques and require supervised data. However, many languages lack such resources. This paper presents an algorithm to automatically dis- cover Named Entities (NEs) in a resource free language, given a bilingual corpora in which it is weakly temporally aligned with a resource rich language. We ob- serve that NEs have similar time distribu- tions across such corpora, and that they are often transliterated, and develop an al- gorithm that exploits both iteratively. The algorithm makes use of a new, frequency based, metric for time distributions and a resource free discriminative approach to transliteration. We evaluate the algorithm on an English-Russian corpus, and show high level of NEs discovery in Russian. 1 Introduction Named Entity recognition has been getting much attention in NLP research in recent years, since it is seen as a significant component of higher level NLP tasks such as information distillation and ques- tion answering, and an enabling technology for bet- ter information access. Most successful approaches to NER employ machine learning techniques, which require supervised training data. However, for many languages, these resources do not exist. Moreover, it is often difficult to find experts in these languages both for the expensive annotation effort and even for language specific clues. On the other hand, compa- rable multilingual data (such as multilingual news streams) are increasingly available (see section 4). In this work, we make two independent observa- tions about Named Entities encountered in such cor- pora, and use them to develop an algorithm that ex- tracts pairs of NEs across languages. Specifically, given a bilingual corpora that is weakly temporally aligned, and a capability to annotate the text in one of the languages with NEs, our algorithm identifies the corresponding NEs in the second language text, and annotates them with the appropriate type, as in the source text. The first observation is that NEs in one language in such corpora tend to co-occur with their coun- terparts in the other. E.g., Figure 1 shows a his- togram of the number of occurrences of the word Hussein and its Russian transliteration in our bilin- gual news corpus spanning years 2001 through late 2005. One can see several common peaks in the two histograms, largest one being around the time of the beginning of the war in Iraq. The word Russia, on the other hand, has a distinctly different temporal signature. We can exploit such weak synchronicity of NEs across languages as a way to associate them. In order to score a pair of entities across languages, we compute the similarity of their time …

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