Informativeness of Inflective Noun Bigrams in Croatian

Jurić, Damir; Banek, Marko; Dembitz, Šandor

doi:10.1007/978-3-642-30947-2_15

Cited by 3 publications

(4 citation statements)

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“…PMI is sensitive to low-frequency intensities and lacks fixed bounds (Bouma, 2009, Jurić et al, 2012). Bouma therefore proposed normalized PMI (NPMI) as (Bouma, 2009):

italicNPMI false(bold-italici false) = italicPMI false(bold-italici false) / - log p false(i false)

where normalization by − log p ( i ) gives lower weight to low frequency intensity pairs.…”

Section: Experimental Methodsmentioning

confidence: 99%

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Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

Reaungamornrat¹,

Liu²,

Wang³

et al. 2013

Phys. Med. Biol.

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Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base of tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam CT (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e., volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC), and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid, and Demons steps was 4.6, 2.1, and 1.7 mm, respectively. The respective ECC was 0.57, 0.70, and 0.73 and NPMI was 0.46, 0.57, and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base of tongue robotic surgery.

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“…PMI is sensitive to low-frequency intensities and lacks fixed bounds (Bouma, 2009, Jurić et al, 2012). Bouma therefore proposed normalized PMI (NPMI) as (Bouma, 2009):

italicNPMI false(bold-italici false) = italicPMI false(bold-italici false) / - log p false(i false)

where normalization by − log p ( i ) gives lower weight to low frequency intensity pairs.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…p(i) = p(i A )p(i B ). PMI is sensitive to low-frequency intensities and lacks fixed bounds (Bouma 2009, Jurić et al 2012. Bouma therefore proposed normalized PMI (NPMI) as (Bouma 2009)…”

Section: Analysis Of Registration Accuracymentioning

confidence: 99%

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

Reaungamornrat¹,

Liu²,

Wang³

et al. 2013

Phys. Med. Biol.

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“…Goldsmith's program Linguistica was used 21 , and annotation standards adopted by Croatian linguists were applied 22 . We focused on common (non-name) words because names in Croatian follow the same inflectional paradigm, but with lower freedom in appearance and with some inflectional restrictions when multiword names are considered 23 .…”

Section: Grammatical N-gram Systemmentioning

confidence: 99%

An Economic Approach to Big Data in a Minority Language

Dembitz

Gledec

Sokele³

2014

Procedia Computer Science

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Google's n-gram project brought recently big data benefits to several main world languages, like English, Chinese etc. Any attempt to derive such systems, aimed to accelerate the development of NLP applications for world minority languages, in the manner in which it has been done in the project, encounters many obstacles. This paper presents an innovative and economic approach to large-scale n-gram system creation applied to the Croatian language case. Instead of using the Web as the world's biggest text repository, our process of n-gram collection relies on the Croatian academic online spellchecker Hascheck, a language service publicly available since 1993 and popular worldwide. The service has already processed a corpus whose size exceeds the size of the Croatian web-corpus created in recent years. Contrary to the Google n-gram systems, where cutoff criteria were applied, our n-gram filtering is based on dictionary criteria. This resulted in a system comparable in size to the largest ngram systems of today. Because of the reliance on a service in constant use, the Croatian n-gram system is a dynamic one, unique among the systems compared. The importance of having an n-gram infrastructure for rapid breakthroughs in new application areas is also exemplified in the paper.

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“…The same can be extended to n-grams, too. In [23] it was shown that rare 2-grams may have higher informativeness than their more frequent counterparts. If statistical [Ivan Perić worked with Renato Šoić.]…”

Section: B Possible Implications Of the Extreme Frequency Differences To Machine Translationsmentioning

confidence: 99%

Dynamic N-Gram System Based on an Online Croatian Spellchecking Service

2019

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As an infrastructure able to accelerate the development of natural language processing applications, large-scale lexical n-gram databases are at present important data systems. However, deriving such systems for world minority languages as it was done in the Google n-gram project leads to many obstacles. This paper presents an innovative approach to large-scale n-gram system creation applied to the Croatian language. Instead of using the Web as the world's largest text repository, our process of n-gram collection relies on the Croatian online academic spellchecker Hascheck, a language service publicly available since 1993 and popular worldwide. Our n-gram filtering is based on dictionary criteria, contrary to the publicly available Google n-gram systems in which cutoff criteria were applied. After 12 years of collecting, the size of the Croatian n-gram system reached the size of the largest Google Version 1 n-gram systems. Due to reliance on a service in constant use, the Croatian n-gram system is a dynamic one. System dynamics allowed modeling of n-gram count behavior through Heaps' law, which led to interesting results. Like many minority languages, the Croatian language suffers from a lack of sophisticated language processing systems in many application areas. The importance of a rich lexical n-gram infrastructure for rapid breakthroughs in new application areas is also exemplified in the paper.

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Informativeness of Inflective Noun Bigrams in Croatian

Cited by 3 publications

References 4 publications

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

An Economic Approach to Big Data in a Minority Language

Dynamic N-Gram System Based on an Online Croatian Spellchecking Service

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