Using grammatical relations to compare parsers

Preiss, Judita

doi:10.3115/1067807.1067846

Cited by 16 publications

(23 citation statements)

References 6 publications

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“…The performance, shown in table 2, is, according to (Preiss, 2003), similar to a large selection of statistical parsers and a grammatical relation finder. Relations involving long-distance dependencies form part of these relations.…”

Section: General Unrestricted Textsmentioning

confidence: 95%

A robust and hybrid deep-linguistic theory applied to large-scale parsing

Schneider

Dowdall

Rinaldi

2004

Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data - ROMAND '04

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Modern statistical parsers are robust and quite fast, but their output is relatively shallow when compared to formal grammar parsers. We suggest to extend statistical approaches to a more deep-linguistic analysis while at the same time keeping the speed and low complexity of a statistical parser. The resulting parsing architecture suggested, implemented and evaluated here is highly robust and hybrid on a number of levels, combining statistical and rule-based approaches, constituency and dependency grammar, shallow and deep processing, full and nearfull parsing. With its parsing speed of about 300,000 words per hour and state-of-the-art performance the parser is reliable for a number of large-scale applications discussed in the article.

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Section: General Unrestricted Textsmentioning

confidence: 95%

A robust and hybrid deep-linguistic theory applied to large-scale parsing

Schneider

Dowdall

Rinaldi

2004

Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data - ROMAND '04

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“…Current precision and recall values for subject, object and PP-attachment relations, and for the disambiguation between prepositions and complements are in table 1. These results, slightly lower than state-of-the-art ( (Lin, 1998), (Preiss, 2003)), are least merit figures or a proof of concept rather than accurate figures. On the one hand, the performance of the parser suffers from mistaggings and mischunkings or a limited grammar, the price for the speed increase.…”

Section: Preliminary Evaluationmentioning

confidence: 55%

A low-complexity, broad-coverage probabilistic dependency parser for English

Schneider

2003

Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Lang

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Large-scale parsing is still a complex and timeconsuming process, often so much that it is infeasible in real-world applications. The parsing system described here addresses this problem by combining finite-state approaches, statistical parsing techniques and engineering knowledge, thus keeping parsing complexity as low as possible at the cost of a slight decrease in performance. The parser is robust and fast and at the same time based on strong linguistic foundations.

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“…For the first result, we apply the standard 500 sentence test set for dependency parsers, GREVAL [16], in order to assess its performance on general text. The results obtained are comparable to other parsers [16-18]. For the second result, we use a random set from the GENIA corpus in order to assess its performance on the biomedical domain.…”

Section: Methods: Corpus Analysismentioning

confidence: 72%