Shared Task Organizing Committee -transliteration Mining: Whitepaper of News 2010 Shared Task on Transliteration Generation Transliteration Generation and Mining with Limited Training Resources Transliteration Using a Phrase-based Statistical Machine Translation System to Re-score the Output of a Jo

ii Preface Named Entities play a significant role in Natural Language Processing and Information Retrieval. While identifying and analyzing named entities in a given natural language is a challenging research problem by itself, the phenomenal growth in the Internet user population, especially among the non-English speaking parts of the world, has extended this problem to the crosslingual arena. We specifically focus The purpose of the NEWS workshop is to bring together researchers across the world interested in identification, analysis, extraction, mining and transformation of named entities in monolingual or multilingual natural language text. The workshop scope includes many interesting specific research areas pertaining to the named entities, such as, orthographic and phonetic characteristics, corpus analysis, unsupervised and supervised named entities extraction in monolingual or multilingual corpus, transliteration modelling, and evaluation methodologies, to name a few. For this years edition, 11 research papers were submitted, each of which was reviewed by at least 3 reviewers from the program committee. 7 papers were chosen for publication, covering main research areas, from named entities recognition, extraction and categorization, to distributional characteristics of named entities, and finally a novel evaluation metrics for co-reference resolution. All accepted research papers are published in the workshop proceedings. This year, as parts of the NEWS workshop, we organized two shared tasks: one on Machine Transliteration Generation, and another on Machine Transliteration Mining, participated by research teams from around the world, including industry, government laboratories and academia. The transliteration generation task was introduced in NEWS 2009. While the focus of the 2009 shared task was on establishing the quality metrics and on baselining the transliteration quality based on those metrics, the 2010 shared task expanded the scope of the transliteration generation task to about dozen languages, and explored the quality depending on the direction of transliteration, between the languages. We collected significantly large, hand-crafted parallel named entities corpora in dozen different languages from 8 language families, and made available as common dataset for the shared task. We published the details of the shared task and the training and development data six months ahead of the conference that attracted an overwhelming response from the research community. Totally 7 teams participated in the transliteration generation task. The approaches ranged from traditional unsupervised learning methods (such as, Phrasal SMT-based, Conditional Random Fields, etc.) to somewhat unique approaches (such as, DirectTL approach), combined with several model combinations for results re-ranking. A report of …