Papers with Part-of-Speech
NoEl: An Annotated Corpus for Noun Ellipsis in English (2020.lrec-1)
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| Challenge: | Ellipsis resolution is an important step to improve the accuracy of mainstream natural language processing tasks such as information retrieval, event extraction, dialog systems, etc. |
| Approach: | They extend the study of ellipsis by annotating a corpus for noun ellippsis and closely related phenomenon using the first hundred movies of Cornell Movie Dialogs Dataset. |
| Outcome: | The proposed corpus has 946 instances of exophoric and endophorical noun ellipsis, making it the biggest resource of nouns in English, to the best of our knowledge. |
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP (2021.acl-demo)
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| Challenge: | Recently, the need for Chinese natural language processing (NLP) has a dramatic increase for many downstream applications. |
| Approach: | They propose to use Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing to train a multi-task model based on a pruned BERT. |
| Outcome: | The proposed model performs better than popular segmentation tools on a non-training corpus. |
A Simple Yet Effective Hybrid Pre-trained Language Model for Unsupervised Sentence Acceptability Prediction (2022.aacl-short)
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| Challenge: | Existing unsupervised prediction approaches rely on language models to estimate sentence acceptability . low-frequency words would have a significant negative impact on sentence likelihood . |
| Approach: | They propose a method that substitutes Part-of-Speech (POS) tags for low-frequency words in sentences . their method improves both a sentence acceptability benchmark and a cross-domain sentence evaluation corpus . |
| Outcome: | The proposed method improves on a sentence acceptability benchmark and a cross-domain sentence evaluation corpus. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
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. |
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings (2021.acl-long)
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| Challenge: | Part-of-Speech (POS) tags are routinely included in many NLP tasks. |
| Approach: | They propose to use POS tags to examine morphological learning in low-resource languages . they find that POS tagging improves joint segmentation and glossing . |
| Outcome: | The proposed task is tested on two identical datasets with the Transformer architecture. |
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)
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| Challenge: | Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization. |
| Approach: | They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context. |
| Outcome: | The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency. |
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging (2022.emnlp-main)
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| Challenge: | Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled training data. |
| Approach: | They propose a method for transferring labels from high-resource sources to low-resourced target languages using a graph-based label propagation method. |
| Outcome: | The proposed method achieves state-of-the-art for unsupervised POS tagging of low-resource languages. |
Analyzing the Role of Part-of-Speech in Code-Switching: A Corpus-Based Study (2024.findings-eacl)
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| Challenge: | Code-switching (CS) is a common linguistic phenomenon wherein speakers fluidly transition between languages in conversation. |
| Approach: | They propose to use a part-of-speech (POS)-based analysis of Spanish-English and Mandarin-English corpora to examine the propensity of bilinguals to engage in CS. |
| Outcome: | The findings confirm the existence of a statistically significant connection between POS and the likelihood of CS across language pairs, but show that it diminishes as tokens distance themselves from CS instances. |
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media (N18-1)
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| Challenge: | Current approaches to Named Entity Recognition (NER) are effective in formal text, but they fail on informal text, where improper grammatical structures, spelling inconsistencies, and slang vocabulary prevail. |
| Approach: | They propose a multitask end-to-end bidirectional long short-term memory (BLSTM)-Conditional Random Field (CRF) network with two CRF classifiers and a feature extractor that transfers learning to a CRF for prediction. |
| Outcome: | The proposed models outperform the current state-of-the-art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments. |
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. |
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks (2024.eacl-long)
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| Challenge: | Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. |
| Approach: | They propose a prompt-based method for token-level sequence labeling tasks . they propose to decompose an input sentence into single tokens and apply one prompt template to each token. |
| Outcome: | The proposed method outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer . the method also attains state-of-the-art performance when employed with the mT5 model . |
Character-Level Feature Extraction with Densely Connected Networks (C18-1)
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| Challenge: | Existing methods to generate character-level features with neural architectures such as CNN or Recurrent Neural Network (RNN) are slow and generate position-independent features. |
| Approach: | They propose a method that uses a densely connected network to extract character-level features from words using CNN and RNN. |
| Outcome: | The proposed method shows robustness and effectiveness while being faster than CNN- or RNN-based methods. |
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging (D18-1)
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| Challenge: | Existing POS tagging methods for Twitter use labeled newswire text . however, Twitter users tend to mimic formal media expressions and develop linguistically informal styles. |
| Approach: | They propose to use newswire text to learn POS tagging for Twitter while twitter users are developing linguistically informal styles. |
| Outcome: | The proposed method achieves better performance than state-of-the-art methods on three different datasets. |
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. |
Toward the Limitation of Code-Switching in Cross-Lingual Transfer (2022.emnlp-main)
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| Challenge: | Recent studies have shown the success of multilingual pretrained models for cross-lingual knowledge transfer. |
| Approach: | They propose to make code-switched sentences replace tokens from multiple languages so they are grammatically consistent . they also consider the similarity between context and the switched tokens to ensure that the newly substituted sentences are grammatically consistent - a limitation that could affect inference . |
| Outcome: | The proposed method outperforms the mBERT and original code-switching method on cross-lingual POS and Named-Entity-Recognition tasks on 30+ languages. |
EMAD: A Bridge Tagset for Unifying Arabic POS Annotations (2024.lrec-main)
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| Challenge: | Existing tagsets for Arabic are difficult to combine due to the diversity of their features. |
| Approach: | They propose an Arabic Extended Morphological Analysis and Disambiguation Tagset which facilitates conversion and unification of Arabic tagsets. |
| Outcome: | The proposed tagset facilitates conversion and unification of different tagsetes used to annotate Arabic corpora. |
Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language (2024.lrec-main)
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| Challenge: | Large Language Models excel in various tasks like Named Entity Recognition and Part-of-Speech tagging. |
| Approach: | They propose to use large language models to perform NLP tasks such as Named Entity Recognition and Part-of-Speech tagging in Nepali. |
| Outcome: | The proposed models perform better than other approaches for Nepali NER and POS tagging tasks. |
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. |
ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language (2024.lrec-main)
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| Challenge: | a new study examines the impact of natural language processing (NLP) on the endangered Manchu language. |
| Approach: | They propose to use BiLSTM-CRF, BERT, and mBERT to train transformer-based models on Manchu for NER and POS tagging tasks. |
| Outcome: | The proposed models achieved over 90% F1 score in both NER and POS tasks. |