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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/18.165464openalex-snapshot
Random texts exhibit Zipf's-law-like word frequency distribution
inferredcites →https://doi.org/10.1109/icsda.2016.7918974openalex-snapshot
Introduction of the Asian Language Treebank
inferredcites →https://doi.org/10.3115/992424.992434openalex-snapshot
Tokenization as the initial phase in NLP
inferredcites →https://doi.org/10.3115/992424.992468openalex-snapshot
Broad coverage automatic morphological segmentation of German words
inferredcites →https://doi.org/10.4324/9780203037232openalex-snapshot
Burmese (Myanmar) Dictionary of Grammatical Forms
inferredcites →https://doi.org/10.63317/2hnj7od8afy8openalex-snapshot
Universal Dependencies v1: A Multilingual Treebank Collection
inferredcites →https://doi.org/10.63317/3pjyez8kmkhzopenalex-snapshot
A Universal Part-of-Speech Tagset
inferredcites →https://doi.org/10.63317/48iewajwhmd3openalex-snapshot
Developing Guidelines and Ensuring Consistency for Chinese Text Annotation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Summary Generation Using Natural Language Processing Techniques and Cosine Similarity
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Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution
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Exploring the nexus between food and veg*n lifestyle via text mining-based online community analytics
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Constituency Parsing by Cross-Lingual Delexicalization
inferred← citeshttps://doi.org/10.1109/ialp51396.2020.9310494openalex-snapshot
A Burmese Dependency Parsing Method Based on Transfer Learning
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Towards Burmese (Myanmar) Morphological Analysis
inferred← citeshttps://doi.org/10.1145/3436818openalex-snapshot
A Neural Joint Model with BERT for Burmese Syllable Segmentation, Word Segmentation, and POS Tagging
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Towards Tokenization and Part-of-Speech Tagging for Khmer: Data and Discussion
inferred← citeshttps://doi.org/10.1145/3578707openalex-snapshot
Impact of Tokenization on Language Models: An Analysis for Turkish
inferred← citeshttps://doi.org/10.1145/3594631openalex-snapshot
Low-resource Multilingual Neural Translation Using Linguistic Feature-based Relevance Mechanisms
inferred← citeshttps://doi.org/10.1162/tacl_a_00343openalex-snapshot
Multilingual Denoising Pre-training for Neural Machine Translation
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“Uncovering Responses”: A Sentiment Analysis Approach to Racial Violence Among Black Parents in Missouri
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Security Implications of Biometric Gesture Recognition
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In Neural Machine Translation, What Does Transfer Learning Transfer?
inferred← citeshttps://doi.org/10.18653/v1/2020.coling-main.376openalex-snapshot
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation
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Goku’s Participation in WAT 2020
inferred← citeshttps://doi.org/10.18653/v1/2021.eacl-main.130openalex-snapshot
The Source-Target Domain Mismatch Problem in Machine Translation
inferred← citeshttps://doi.org/10.18653/v1/2021.wat-1.1openalex-snapshot
Overview of the 8th Workshop on Asian Translation
inferred← citeshttps://doi.org/10.18653/v1/d19-5201openalex-snapshot
Overview of the 6th Workshop on Asian Translation
inferred← citeshttps://doi.org/10.18653/v1/d19-5213openalex-snapshot
Facebook AI’s WAT19 Myanmar-English Translation Task Submission
inferred← citeshttps://doi.org/10.18653/v1/d19-5219openalex-snapshot
Sarah’s Participation in WAT 2019
inferred← citeshttps://doi.org/10.46223/hcmcoujs.tech.en.12.1.2217.2022openalex-snapshot
Khmer printed character recognition using attention-based Seq2Seq network
inferred← citeshttps://doi.org/10.48550/arxiv.2103.16801openalex-snapshot
Joint Khmer Word Segmentation and Part-of-Speech Tagging Using Deep Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2109.03784openalex-snapshot
A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data
inferred← citeshttps://doi.org/10.62019/abbdm.v4i1.111openalex-snapshot
Part-Of-Speech Tagging for Balochi Language: A Data driven application of Conditional Random Fields
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