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One model for the learning of language.
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In: Proceedings of the National Academy of Sciences of the United States of America, vol 119, iss 5 (2022)
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Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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Arc-Eager Construction Provides Learning Advantage Beyond Stack Management
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Controlled Multilingual Thesauri for Kazakh Industry-Specific Terms
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In: Social Inclusion ; 9 ; 1 ; 35-44 ; Social Inclusion and Multilingualism: The Impact of Linguistic Justice, Economy of Language and Language Policy (2021)
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Assembling Syntax: Modeling Constituent Questions in a Grammar Engineering Framework
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THE FUTURE TENSE PROPERTIES of UYGHUR and TURKISH
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In: Zeitschrift für die Welt der Türken / Journal of World of Turks; Vol 12, No 2 (2020): [ZFWT] VOL. 12, NO. 2 (2020); 69-80 (2020)
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Linguistic Phylogeny with Bayesian Markov Chain Monte Carlo: The Case of Indo-European
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Issues in Named Entity Recognition on Early Modern English Letters
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Abstract:
Thesis (Master's)--University of Washington, 2019 ; The influx of digitized historical documents into online collections has made the study of these documents much more accessible to researchers and the general public. This data, however, is frequently raw data sometimes obtained through automated methods such as optical character recognition. Without rich metadata, the content of these documents is difficult to search and organize. Tasks commonly undertaken in the field of computational linguistics can aid in this endeavour. These documents often present challenges for modern systems, however, as the text contained in historical documents frequently differs in many ways from the present-day newswire these systems are most often trained on. In this thesis I explore the task of Named Entity Recognition on texts written in Early Modern English. I investigate three methodologies for bootstrapping training data to train a character-based neural net model. The results show substantial improvements upon all baselines, with the best f-measure at 60.31%
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Keyword:
brown cluster; computational linguistics; Computer science; digital humanities; early modern english; History; Linguistics; named entity recognition; neural net
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URL: http://hdl.handle.net/1773/44845
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Detection of Longitudinal Development of Dementia in Literary Writing
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In: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1524651391474684 (2018)
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Movement and structure effects on Universal 20 word order frequencies: A quantitative study
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In: Glossa: a journal of general linguistics; Vol 3, No 1 (2018); 84 ; 2397-1835 (2018)
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Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications
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In: Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC) ; International Conference on Language Resources and Evaluation (LREC) ; 11 (2018)
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Mining Social Science Publications for Survey Variables
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In: Proceedings of the Second Workshop on NLP and Computational Social Science ; 47-52 (2018)
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Resonances in Middle High German: New Methodologies in Prosody
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In: Hench, Christopher Leo. (2017). Resonances in Middle High German: New Methodologies in Prosody. UC Berkeley: German. Retrieved from: http://www.escholarship.org/uc/item/13c6h2z2 (2017)
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The Influence of Syntactic Frequencies on Human Sentence Processing
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1502452939626929 (2017)
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Learning novel phonotactics from exposure to continuous speech
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 8, No 1 (2017); 12 ; 1868-6354 (2017)
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Machine-readable text corpora and the linguistic description of languages
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In: Text analysis and computers ; 1 ; ZUMA-Nachrichten Spezial ; 64-75 ; Text Analysis and Computers Conference (2017)
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Code-switched English Pronunciation Modeling for Swahili Spoken Term Detection (Pub Version, Open Access)
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Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns
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Making the Most of It: Word Sense Annotation and Disambiguation in the Face of Data Sparsity and Ambiguity
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In: Jurgens, David Alan. (2014). Making the Most of It: Word Sense Annotation and Disambiguation in the Face of Data Sparsity and Ambiguity. UCLA: Computer Science 0201. Retrieved from: http://www.escholarship.org/uc/item/2wn4h7ph (2014)
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