| Challenge: | a corpus of more than 31,000 tokens is used for part-of-speech tagging in Albanian . a large number of multi-word units are difficult to tally, especially when they have articles or particles as their first part. |
| Approach: | They propose a gold standard corpus for Albanian part-of-speech tagging and perform evaluation experiments with different statistical taggers. |
| Outcome: | The proposed corpus can accurately represent the syntagmatic aspects of Albanian . the results show that the standard is accurate on both the full and coarse tagsets . |
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Antonios Anastasopoulos, Marika Lekakou, Josep Quer, Eleni Zimianiti, Justin DeBenedetto, David Chiang
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How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project. (N19-1)
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| Challenge: | Using annotation variation principles, Part-of-Speech tagging performance degrades when applied to test sentences that depart from training data. |
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| Challenge: | a new corpus for detecting and linking survey variables is being developed . the corpus is multilingual and includes manually curated word and phrase alignments . |
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Part-of-speech Tagging for Extremely Low-resource Indian Languages (2024.findings-acl)
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| Challenge: | Modern natural language processing systems thrive when given access to large datasets, but a large fraction of the world’s languages are not privy to such benefits due to sparse documentation and inadequate digital representation. |
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A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web (2020.lrec-1)
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| Challenge: | Part-of-speech tagging is a fundamental part of NLP, but it is not widely used in unstructured text processing. |
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MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages (2023.acl-long)
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Where are we Still Split on Tokenization? (2024.findings-eacl)
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| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
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A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
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| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
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LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
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Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
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