Charting the depths of robust speech parsing

Kasper, Walter; Kiefer, Bernd; Krieger, Hans‐Ulrich; Rupp, C. J.; Worm, Karsten L.

doi:10.3115/1034678.1034741

Cited by 17 publications

(18 citation statements)

References 8 publications

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“…Furthermore, our current level of success has been achieved without significant changes to the original grammar and, once we start to tailor the grammar to the domain, we will gain further significant increases in performance. As a final stage, we may find it useful to follow Kasper et al (1999) and have a 'fallback' strategy for failed parses where the best partial analyses are assembled in a robust processing phase.…”

Section: Evaluation and Future Researchmentioning

confidence: 99%

XML-based data preparation for robust deep parsing

Grover

Lascarides

2001

Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01

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We describe the use of XML tokenisation, tagging and markup tools to prepare a corpus for parsing. Our techniques are generally applicable but here we focus on parsing Medline abstracts with the ANLT wide-coverage grammar. Hand-crafted grammars inevitably lack coverage but many coverage failures are due to inadequacies of their lexicons. We describe a method of gaining a degree of robustness by interfacing POS tag information with the existing lexicon. We also show that XML tools provide a sophisticated approach to pre-processing, helping to ameliorate the 'messiness' in real language data and improve parse performance.

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Section: Evaluation and Future Researchmentioning

confidence: 99%

XML-based data preparation for robust deep parsing

Grover

Lascarides

2001

Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01

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“…The algorithm runs with a worst-case time linear in the number of edges and vertices. In [23], different methods for partial parse selection are presented and evaluated for HPSG parsers producing much deeper analyses than the parser presented here. The criteria examined for selecting partial parsers include longest edges and shortest paths using several weight estimation functions based on probabilities derived from treebanks.…”

Section: Fig 3 Transferred Dependency Tree (Left) and Its Corresponmentioning

confidence: 99%

Integration of a Spanish-to-LSE Machine Translation System into an e-learning Platform

López-Colino¹,

Tejedor²,

Porta³

et al. 2011

Lecture Notes in Computer Science

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. This paper presents the first results of the integration of a Spanish-to-LSE Machine Translation (MT) system into an e-learning platform. Most elearning platforms provide speech-based contents, which makes them inaccessible to the Deaf. To solve this issue, we have developed a MT system that translates Spanish speech-based contents into LSE. To test our MT system, we have integrated it into an e-learning tool. The elearning tool sends the audio to our platform. The platform sends back the subtitles and a video stream with the signed translation to the e-learning tool. Preliminary results, evaluating the sign language synthesis module, show an isolated sign recognition accuracy of 97%. The sentence recognition accuracy was of 93%.

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“…Other properties, such as prosodic information or probabilistic scores could also be utilized in the estimation function. A detailed description of the approach can be found in (Kasper et al, 1999). Note that the paths PR and QR are chosen, but not ST, although S is the longest edge.…”

Section: Computing Best Partial Analysesmentioning

confidence: 99%