Papers with PoS

13 papers
A Study on Using Semantic Word Associations to Predict the Success of a Novel (2021.starsem-1)

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Challenge: Existing methods for book success prediction are not effective.
Approach: They propose to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embeddment for a set of semantically linked word clusters.
Outcome: The proposed method outperforms the previous methods for book success prediction.
Building a Part-of-Speech Tagged Corpus for Drenjongke (Bhutia) (2020.aacl-srw)

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Challenge: a corpus of sentences and 1379 tokens were generated for the first Drenjongke corpus . the language is considered "vulnerable," "definitely endangered" and "severely endangered."
Approach: They propose to generate the first corpus of the Tibetan language using a phrase book . they propose to use 34 Part-of-Speech (PoS) tags to define the first Drenjongke corpus .
Outcome: The first corpus of the Drenjongke language comprises 275 sentences and 1379 tokens . the paper plans to expand with other materials to promote further studies of the language .
Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)

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Challenge: Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high or in low-resource scenarios.
Approach: They examine the impact of PoS tags on graph- and transition-based parsers and propose to use them in a new paradigm for sequence labeling.
Outcome: The proposed model is best when tagging accuracy and resource availability are high.
SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing (2022.findings-acl)

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Challenge: Recent work on code mixing in computational settings has leveraged social media code mixed texts to train NLP models.
Approach: They propose to use language ID tags to measure syntactic variety in code-mixed text and their relationship with computational model performance.
Outcome: The proposed measure can be applied to English(en)-hindi(hi) code-mixed datasets and compares them with other measures.
AlignFreeze: Navigating the Impact of Realignment on the Layers of Multilingual Models Across Diverse Languages (2025.naacl-short)

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Challenge: Realignment techniques are often employed to enhance cross-lingual transfer in multilingual language models, but can degrade performance in languages that differ significantly from the fine-tuned source language.
Approach: They propose a method that freezes either the lower half or upper half of the layers during realignment to prevent performance degradation.
Outcome: The proposed method improves Part-of-Speech (PoS) tagging performance in languages where realignment fails.
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms (2022.lrec-1)

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Challenge: Potential Idiomatic Expression (PIE) dataset for NLP in English contains over 20,100 samples with almost 1,200 cases of idioms from 10 classes (or senses).
Approach: They present a large Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English.
Outcome: The proposed dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses).
PortOldBERT: Portuguese Historical Language Models (2026.eacl-long)

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Challenge: Historical language models are important for many tasks, but have limited application in historical Portuguese.
Approach: They propose a Portuguese encoder language model that integrates historical linguistic contexts while retaining the ability to process contemporary text.
Outcome: The proposed model outperforms Albertina on multiple tasks and for different historical periods.
Decoding Part-of-Speech from Human EEG Signals (2022.acl-long)

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Challenge: Recent studies have shown that EEG signal magnitude and topography depend on word length, frequency and open vs. closed class.
Approach: They propose to use EEG to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution during text reading.
Outcome: The proposed techniques outperform linear-SVMs on PoS tagging of unigram and bigram data.
Who Taught You That? Tracing Teachers in Model Distillation (2025.findings-acl)

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Challenge: Xu et al., 2006, show that model distillation can imbue efficient small language models with task-specific capabilities competitive with expensive teacher LLMs.
Approach: They propose to distill outputs from a large teacher model to a small student model . they propose to use part-of-speech templates as higher-order linguistic features capable of capturing distinctive signals from teacher models that persist in distilled student outputs.
Outcome: The proposed model distillation technique can imbue efficient small language models with task-specific capabilities competitive with (expensive) teacher LLMs.
Korean Language Modeling via Syntactic Guide (2022.lrec-1)

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Challenge: Existing research on pre-trained language models focuses on widely-used languages . however, not every language can benefit from such models due to computational resources .
Approach: They propose to build a pre-trained language model that understands the linguistic phenomena in the target language with low resources.
Outcome: The proposed model improves the performance of Korean language understanding tasks.
Urdu Pitch Accents and Intonation Patterns in Spontaneous Conversational Speech (2020.lrec-1)

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Challenge: Recent studies of Urdu intonation describe scripted and laboratory speech .
Approach: They summarise Urdu pitch accents and their intonation patterns using a simplified version of the Rhythm and Pitch labelling system and a simple RAP system.
Outcome: The analysis of a hand-labelled telephone conversation shows that low pitch accents play an important role in Urdu spontaneous speech.
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction (2024.lrec-main)

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Challenge: Open Information Extraction (OIE) is a structure prediction task in NLP that aims to extract structured n-ary tuples from free text.
Approach: They propose to leverage linguistic features with a Seq2Seq PLM for OIE to improve performance.
Outcome: The proposed methods give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go.
Convergence and Divergence of Language Models under Different Random Seeds (2025.emnlp-main)

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Challenge: a large body of work has examined the training dynamics of language models.
Approach: They investigate the convergence of language models (LMs) trained under different random seeds . they find that larger models reconverge faster in later training stages, while smaller models never actually reconverge.
Outcome: The proposed model size and training checkpoints influence convergence of language models under different seeds.

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