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    <titleInfo>
        <title>A Neural Pipeline for POS-tagging and Lemmatizing Cuneiform Languages</title>
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    <name type="personal">
        <namePart type="given">Aleksi</namePart>
        <namePart type="family">Sahala</namePart>
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        <namePart type="family">Lindén</namePart>
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        <dateIssued>2023-09</dateIssued>
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            <title>Proceedings of the Ancient Language Processing Workshop</title>
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        <name type="personal">
            <namePart type="given">Adam</namePart>
            <namePart type="family">Anderson</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Shai</namePart>
            <namePart type="family">Gordin</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Bin</namePart>
            <namePart type="family">Li</namePart>
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        <name type="personal">
            <namePart type="given">Yudong</namePart>
            <namePart type="family">Liu</namePart>
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        <name type="personal">
            <namePart type="given">Marco</namePart>
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            <namePart type="family">Passarotti</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>We presented a pipeline for POS-tagging and lemmatizing cuneiform languages and evaluated its performance on Sumerian, first millennium Babylonian, Neo-Assyrian and Urartian texts extracted from Oracc. The system achieves a POS-tagging accuracy between 95-98% and a lemmatization accuracy of 94-96% depending on the language or dialect. For OOV words only, the current version can predict correct POS-tags for 83-91%, and lemmata for 68-84% of the input words. Compared with the earlier version, the current one has about 10% higher accuracy in OOV lemmatization and POS-tagging due to better neural network performance. We also tested the system for lemmatizing and POS-tagging the PROIEL Ancient Greek and Latin treebanks, achieving results similar to those with the cuneiform languages.</abstract>
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        <url>https://aclanthology.org/2023.alp-1.23</url>
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        <date>2023-09</date>
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            <start>203</start>
            <end>212</end>
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%0 Conference Proceedings
%T A Neural Pipeline for POS-tagging and Lemmatizing Cuneiform Languages
%A Sahala, Aleksi
%A Lindén, Krister
%Y Anderson, Adam
%Y Gordin, Shai
%Y Li, Bin
%Y Liu, Yudong
%Y Passarotti, Marco C.
%S Proceedings of the Ancient Language Processing Workshop
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F sahala-linden-2023-neural
%X We presented a pipeline for POS-tagging and lemmatizing cuneiform languages and evaluated its performance on Sumerian, first millennium Babylonian, Neo-Assyrian and Urartian texts extracted from Oracc. The system achieves a POS-tagging accuracy between 95-98% and a lemmatization accuracy of 94-96% depending on the language or dialect. For OOV words only, the current version can predict correct POS-tags for 83-91%, and lemmata for 68-84% of the input words. Compared with the earlier version, the current one has about 10% higher accuracy in OOV lemmatization and POS-tagging due to better neural network performance. We also tested the system for lemmatizing and POS-tagging the PROIEL Ancient Greek and Latin treebanks, achieving results similar to those with the cuneiform languages.
%U https://aclanthology.org/2023.alp-1.23
%P 203-212

Markdown (Informal)

[A Neural Pipeline for POS-tagging and Lemmatizing Cuneiform Languages](https://aclanthology.org/2023.alp-1.23) (Sahala & Lindén, ALP-WS 2023)

• A Neural Pipeline for POS-tagging and Lemmatizing Cuneiform Languages (Sahala & Lindén, ALP-WS 2023)

ACL

• Aleksi Sahala and Krister Lindén. 2023. A Neural Pipeline for POS-tagging and Lemmatizing Cuneiform Languages. In Proceedings of the Ancient Language Processing Workshop, pages 203–212, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.