Enhancing Sumerian Lemmatization by Unsupervised Named-Entity Recognition

Liu, Yudong; Burkhart, Clinton; Hearne, James; Luo, Liang

doi:10.3115/v1/n15-1167

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…However, the field does have a tradition with dictionarybased glossing of transliterated text. Similar to technologies commonly used in language documentation and linguistic typology (Robinson et al, 2007), the ORACC Lemmatizer (Robson, 2018;Liu et al, 2015) can provide word-by-word glosses along with a morphological analysis, albeit without contextual disambiguation, and without producing coherent text.…”

Section: Related Workmentioning

confidence: 99%

Towards the First Machine Translation System for Sumerian Transliterations

Punia¹,

Schenk²,

Chiarcos³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.

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Section: Related Workmentioning

confidence: 99%

Towards the First Machine Translation System for Sumerian Transliterations

Punia¹,

Schenk²,

Chiarcos³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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show abstract

“…-Many works go further into some specific domains, such as Sci-Tech compound entity recognition (Yan et al 2016), biomedical named entity recognition (Song et al 2018), entity extraction from clinical records (Alicante et al 2016;Boytcheva 2018;Henriksson et al 2015), chemical named entity recognition (Swain and Cole 2016; Zhang et al 2016), educational term extraction (Conde et al 2016), cybersecurity concepts extraction (Xiao 2017), Drug Name Recognition (Liu et al 2015b), medical named entity recognition (He and Kayaalp 2008;Kavuluru et al 2013;Skeppstedt 2014), and so forth. -In addition to English, unsupervised Named Entity Recognition has been studied in other languages, including Chinese (Jia et al 2018), Spanish (Copara et al 2016), French (Mosallam et al 2014), Italian (Alicante et al 2016), and Russian (Ivanitskiy et al 2016), as well as crosslingual (Abdel Hady et al 2014), even including a dead language like Sumerian (Liu et al 2015a).…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Liao

Zhao

2019

ACM Comput. Surv.

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Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.

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“…Our morphological analyzer will be partly based on existing tools such as Tablan et al ( 2006)'s rule-based morphology and Liu et al (2015)'s algorithm to identify named entities. We will design a custom parser for numero-metrological content for the occasion.…”

Section: Morphological Analysismentioning

confidence: 99%

Machine Translation and Automated Analysis of the Sumerian Language

Pagé-Perron¹,

Sukhareva²,

Khait³

et al. 2017

Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanit

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This paper presents a newly funded international project for machine translation and automated analysis of ancient cuneiform 1 languages where NLP specialists and Assyriologists collaborate to create an information retrieval system for Sumerian. 2 This research is conceived in response to the need to translate large numbers of administrative texts that are only available in transcription, in order to make them accessible to a wider audience. The methodology includes creation of a specialized NLP pipeline and also the use of linguistic linked open data to increase access to the results.

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Enhancing Sumerian Lemmatization by Unsupervised Named-Entity Recognition

Cited by 9 publications

References 9 publications

Towards the First Machine Translation System for Sumerian Transliterations

Towards the First Machine Translation System for Sumerian Transliterations

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Machine Translation and Automated Analysis of the Sumerian Language

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