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A Revised Comparison of Polish Taggers in the Application for Automatic Speech Recognition

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Human Language Technology. Challenges for Computer Science and Linguistics (LTC 2013)

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In this paper (This is a revised and extended version of the article A Comparison of Polish Taggers in the Application for Automatic Speech Recognition that appeared in the Proceedings of Language and Tools Conference, Poznan, 2013.) we investigate the performance of Polish taggers in the context of automatic speech recognition (ASR). We use a morphosyntactic language model to improve speech recognition in an ASR system and seek the best Polish tagger for our needs. Polish is an inflectional language and an n-gram model using morphosyntactic features, which reduces data sparsity seems to be a good choice. We investigate the difference between the morphosyntactic taggers in that context. We compare the results of tagging with respect to the reduction of word error rate as well as speed of tagging. As it turns out at present the taggers using conditional random fields (CRF) models perform the best in the context of ASR. A broader audience might be also interested in the other discussed features of the taggers such as easiness of installation and usage, which are usually not covered in the papers describing such systems.

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  1. 1.

    We use the terms part-of-speech and grammatical class interchangeably in this document, due to the way they are used in the literature regarding Polish tagsets and taggers.

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    We have not included the results for WMBT since it was impossible to obtain its results when these tests were performed. Moreover its behaviour was the worst in all the other tests, so we have not expected to see any improvement.


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This work was supported by LIDER/37/69/L-3/11/NCBR/2012 grant.

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Correspondence to Aleksander Smywiński-Pohl .

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Smywiński-Pohl, A., Ziółko, B. (2016). A Revised Comparison of Polish Taggers in the Application for Automatic Speech Recognition. In: Vetulani, Z., Uszkoreit, H., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2013. Lecture Notes in Computer Science(), vol 9561. Springer, Cham.

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