Christian Monson


2011

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Book Review: A Resource-Light Approach to Morpho-Syntactic Tagging by Anna Feldman and Jirka Hana
Christian Monson
Computational Linguistics, Volume 37, Issue 1 - March 2011

2010

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EMMA: A novel Evaluation Metric for Morphological Analysis
Sebastian Spiegler | Christian Monson
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2008

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Evaluating an Agglutinative Segmentation Model for ParaMor
Christian Monson | Alon Lavie | Jaime Carbonell | Lori Levin
Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology

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Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages
Christian Monson | Ariadna Font Llitjós | Vamshi Ambati | Lori Levin | Alon Lavie | Alison Alvarez | Roberto Aranovich | Jaime Carbonell | Robert Frederking | Erik Peterson | Katharina Probst
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Producing machine translation (MT) for the many minority languages in the world is a serious challenge. Minority languages typically have few resources for building MT systems. For many minor languages there is little machine readable text, few knowledgeable linguists, and little money available for MT development. For these reasons, our research programs on minority language MT have focused on leveraging to the maximum extent two resources that are available for minority languages: linguistic structure and bilingual informants. All natural languages contain linguistic structure. And although the details of that linguistic structure vary from language to language, language universals such as context-free syntactic structure and the paradigmatic structure of inflectional morphology, allow us to learn the specific details of a minority language. Similarly, most minority languages possess speakers who are bilingual with the major language of the area. This paper discusses our efforts to utilize linguistic structure and the translation information that bilingual informants can provide in three sub-areas of our rapid development MT program: morphology induction, syntactic transfer rule learning, and refinement of imperfect learned rules.

2007

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ParaMor: Minimally Supervised Induction of Paradigm Structure and Morphological Analysis
Christian Monson | Jaime Carbonell | Alon Lavie | Lori Levin
Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology

2004

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Unsupervised Induction of Natural Language Morphology Inflection Classes
Christian Monson | Alon Lavie | Jaime Carbonell | Lori Levin
Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology

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A Framework for Unsupervised Natural Language Morphology Induction
Christian Monson
Proceedings of the ACL Student Research Workshop

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Augmenting Manual Dictionaries for Statistical Machine Translation Systems
Stephan Vogel | Christian Monson
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Data Collection and Analysis of Mapudungun Morphology for Spelling Correction
Christian Monson | Lori Levin | Rodolfo Vega | Ralf Brown | Ariadna Font Llitjos | Alon Lavie | Jaime Carbonell | Eliseo Cañulef | Rosendo Huisca
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2002

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Automatic rule learning for resource-limited MT
Jaime Carbonell | Katharina Probst | Erik Peterson | Christian Monson | Alon Lavie | Ralf Brown | Lori Levin
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers

Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and refines transfer rules. A rule-generalization lattice is defined based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples.