(2019), Paleo codage - a machine-readable way to describecuneiform characters paleographically, Utrecht, Netherlands.URL: 1, 3Homburg, T. (2017), Postagging and semantic dictionary creation for hittitecuneiform, in ‘DH2017’. (2006), ‘Predictive text computer simplified keyboard with word andphrase auto-completion (plus text-to-speech and a foreign language translationoption)’. (2014), Computer-assisted reconstruction of virtual fragmented cuneiformtablets, in ‘2014 International Conference on Virtual Systems & Multimedia(VSMM)’, IEEE, pp. (2017), A virtual 3d cuneiform tablet reconstruc-tion interaction, in ‘Proceedings of the 31st British Computer Society HumanComputer Interaction Conference’, BCS Learning & Development Ltd., p. I., Ch’ng, E., Hernandez-Munoz, L., Gehlken, E., Nash,D., Lewis, A. (2015), Neural network language model forchinese pinyin input method engine, in ‘Proceedings of the 29th Pacific AsiaConference on Language, Information and Computation’, pp. Next likely options are aperson named Enlil (male or female), the people (tribe) of Enlil, or a locationĬhen, S., Zhao, H.
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The dictionary knows, that Enlilis a gods name (NE) and is commonly preceded by a determinative character for god?(an), which is suggested in first place to fill the gap. (2015) and will provide a self-learning component. The implementation builds up on the concept of input method enginesHomburg et al. 1)displaying the results of the machine learning process which is currently in devel-opment. Lastly, the poster presents a prototypical application (fig. A possible futuregoal could be a shared task to improve classification accuracy similar to thecuneiform language identification challenge Jauhiainen et al. Theposter features selected peliminary results of the classification and a significanceanalysis of the features for further discussion for improvement. Texts are prepared withrandom gaps for classification and evaluated using the original texts (the goldstandard) on unicode cuneiform and on the respective cuneiform transliterationfor different cuneiform languages (Sumerian, Hittite, Akkadian) and epochs. The effectiveness of the algorithms and features is tested on a corpus of all CDLItexts in ATF which is split in a training and test set. text categorizations– Paleographic Features using PaleoCodage for a subset of manually annotatedtexts Homburg (2019) for Hidden Markov Model Classifications– Grammatical features derived from POSTaggers– Semantic Features derived from the semantic meaning of surrounding words– Metadata Features e.g. (2015).įollowing Homburg & Chiarcos (2016) machine learning methods applied areeither based on grammatical rules (POSTagging), dictionary-based methods ex-ploiting (third-party) dictionary resources or statistical approaches using thefollowing types of machine learning features:– Context-dependent features: e.g. Inputmethod engines for cuneiform have been developed by Homburg et al. (2018) also with machine learning approaches and neural networks.
![cuneiform input method cuneiform input method](https://www.researchgate.net/profile/Timo-Homburg/publication/331609445/figure/fig2/AS:734333287530496@1552090107131/Hittite-POSTagger_Q320.jpg)
Those tech-nologies are heavily relied on in input method engines1 which are powered withdifferent dictionary-based algorithms, but recently Chen et al. Related work has been done in autocompletion systems which face the similarchallenge of anticipating the users input derived from context and other featuresLeung & Zhang (2008), Gikandi (2006), Hyvönen & Mäkelä (2006).
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The classification results are to be used to create a epochand language specific recommendation system to fill gaps on cuneiform tablets,therefore assisting cuneiform scholars. With the emergence of more digitally availablecuneiform text resources, this publication sees an opportunity to investigate ifauto-complete algorithms, based on machine learning and linguistic linked opendata (LLOD) resources Homburg (2017) can be useful in the reconstructionof cuneiform texts. These fractures or gaps in the cuneiformtablet are not always easy for scholars to fill and take a considerable amount ofinterpretation time on their part. However,not always broken fragments can complement each other and often parts of thecuneiform tablet remain destroyed. (2010), to paleographically describe Homburg (2019) and to digitally recon-struct broken fragments Collins et al. In recent years efforts have been undertaken to 3DScan Maraet al. A presisting problem in near eastern studies is the existence of broken cuneiformtablets (listing 1.1).