A non-projective dependency parser

Tapanainen, Pasi; Järvinen, Timo

doi:10.3115/974557.974568

Cited by 197 publications

(104 citation statements)

References 10 publications

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“…The same proportion of texts is kept for both selected training and test datasets. In order to extract the feature vector for the learning process, all the texts of the corpora were parsed with the Connexor Machinese [17], which provides the dependency parser for the Spanish language model.…”

Section: -Corpus Of Medical Translations By Professionals (Mtp) Whicmentioning

confidence: 99%

Identification of Translationese: A Machine Learning Approach

Ilisei

Inkpen

Pastor

et al. 2010

Computational Linguistics and Intelligent Text Processing

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Abstract. This paper presents a machine learning approach to the study of translationese. The goal is to train a computer system to distinguish between translated and non-translated text, in order to determine the characteristic features that influence the classifiers. Several algorithms reach up to 97.62% success rate on a technical dataset. Moreover, the SVM classifier consistently reports a statistically significant improved accuracy when the learning system benefits from the addition of simplification features to the basic translational classifier system. Therefore, these findings may be considered an argument for the existence of the Simplification Universal.

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Section: -Corpus Of Medical Translations By Professionals (Mtp) Whicmentioning

confidence: 99%

Identification of Translationese: A Machine Learning Approach

Ilisei

Inkpen

Pastor

et al. 2010

Computational Linguistics and Intelligent Text Processing

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“…The text is parsed using Connexor's English parser (Tapanainen and Jarvinen 1997). The resulting set of parse trees is traversed and parent-child instance (lexeme) pairs are counted.…”

Section: Structural N-gram Model Statistical Surface N-grammentioning

confidence: 99%

Symbolic-to-statistical hybridization: extending generation-heavy machine translation

Habash

Dorr

Monz

2009

Machine Translation

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The last few years have witnessed an increasing interest in hybridizing surface-based statistical approaches and rule-based symbolic approaches to machine translation (MT). Much of that work is focused on extending statistical MT systems with symbolic knowledge and components. In the brand of hybridization discussed here, we go in the opposite direction: adding statistical bilingual components to a symbolic system. Our base system is Generation-heavy machine translation (GHMT), a primarily symbolic asymmetrical approach that addresses the issue of Interlingual MT resource poverty in source-poor/target-rich language pairs by exploiting symbolic and statistical target-language resources. GHMT's statistical components are limited to target-language models, which arguably makes it a simple form of a hybrid system. We extend the hybrid nature of GHMT by adding statistical bilingual components. We also describe the details of retargeting it to Arabic-English MT. The morphological richness of Arabic brings several challenges to the hybridization task. We conduct an extensive evaluation of multiple system variants. Our evaluation shows that this new variant of GHMT-a primarily symbolic system extended with monolingual and bilingual statistical components-has a higher degree of grammaticality than a phrase-based statistical MT system, where grammaticality is measured in terms of correct verb-argument realization and long-distance dependency translation.

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“…Our English system proceeds by parsing the set of possibly relevant sentences, as well as the topic document sentences, with the Conexor's FDG Parser [7,5]. For the two sets of sentences and supporting document titles, the time expressions (TEs) are also identified and resolved using the temporal expression recogniser and normaliser previously developed by one of the authors [6].…”

Section: System Descriptionmentioning

confidence: 99%

University of Alicante at WiQA 2006

Ruiz

Puşcaşu

Monteagudo

et al. 2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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This paper presents the participation of University of Alicante at the WiQA pilot task organized as part of the CLEF 2006 campaign. For a given set of topics, this task presupposes the discovery of important novel information distributed across different Wikipedia entries. The approach we adopted for solving this task uses Information Retrieval, query expansion by feedback, novelty re-ranking, as well as temporal ordering. Our system has participated both in the Spanish and English monolingual tasks. For each of the two participations the results are promising because, by employing a language independent approach, we obtain scores above the average. Moreover, in the case of Spanish, our result is very close to the best achieved score. Apart from introducing our system, the present paper also provides an in-depth result analysis, and proposes future lines of research, as well as follow-up experiments.

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A non-projective dependency parser

Cited by 197 publications

References 10 publications

Identification of Translationese: A Machine Learning Approach

Identification of Translationese: A Machine Learning Approach

Symbolic-to-statistical hybridization: extending generation-heavy machine translation

University of Alicante at WiQA 2006

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