A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese (2025.coling-demos)
Copied to clipboard
| Challenge: | Existing tools for word segmentation are based on different linguistic theories or target different scenarios. |
| Approach: | They propose a probabilistic toolkit for multi-grained word segmentation in Chinese . they adopt semi-Markov CRF for single-grain word segmenting (SWS) . |
| Outcome: | The proposed approach can produce marginal probabilities of words during inference and significantly improve performance in the cross-domain scenario. |
Similar Papers
Multi-grained Chinese Word Segmentation with Weakly Labeled Data (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on single-grained word segmentation (SWS) focuses on segmenting a sentence into multiple word sequences to preserve all words of different granularities. |
| Approach: | They propose to use a graph-based parser to accommodate weakly labeled data for MWS by employing a simple yet competitive graph-basic parsers with local loss. |
| Outcome: | The proposed model outperforms the state-of-the-art model on weakly labeled data on a high-quality dataset from canonical newswire (NEWS) and non-canonical web (BAIKE) data. |
CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing methods for Chinese word segmentation have achieved state-of-the-art performance, but they pose challenges in the deployment. |
| Approach: | They propose to augment PLM-based Chinese word segmentation schemes by developing cohort training and versatile decoding strategies. |
| Outcome: | The proposed model can be used to augment existing PLM-based models and improve their performance on Chinese LLaMA and Alpaca datasets. |
Advancing Multi-Criteria Chinese Word Segmentation Through Criterion Classification and Denoising (2023.acl-long)
Copied to clipboard
| Challenge: | Recent research on multi-criteria Chinese word segmentation focuses on building complex private structures, adding more handcrafted features, or introducing complex optimization processes. |
| Approach: | They propose a model that fits multiple Chinese word segments using input-hint inputs. |
| Outcome: | The proposed model achieves state-of-the-art (SoTA) performance on multiple datasets simultaneously. |
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)
Copied to clipboard
| Challenge: | Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data. |
| Approach: | They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data. |
| Outcome: | The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy. |
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to unsupervised Chinese word segmentation (CWS) are discriminative and generative, but they are non-trivial. |
| Approach: | They propose a neural generative model for fully unsupervised Chinese word segmentation (CWS) their approach explicitly focuses on the segmental nature of Chinese, and preserves several properties of language models. |
| Outcome: | The proposed model achieves competitive performance to the state-of-the-art models on four datasets from SIGHAN 2005 bakeoff. |
RethinkCWS: Is Chinese Word Segmentation a Solved Task? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have seen remarkable success in the use of deep neural networks on Chinese word segmentation (CWS) however, the performance of CWS systems has gradually reached a plateau with the rapid development of deep networks. |
| Approach: | They propose a fine-grained evaluation for existing Chinese word segmentation systems that allows us to diagnose the strengths and weaknesses of existing models. |
| Outcome: | The proposed model can diagnose strengths and weaknesses of existing models and alleviate negative transfer problem when doing multi-criteria learning. |
Improving Chinese Named Entity Recognition with Multi-grained Words and Part-of-Speech Tags via Joint Modeling (2024.lrec-main)
Copied to clipboard
| Challenge: | Named entity recognition (CNER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose a tree parsing approach for jointly modeling Chinese named entity recognition (CNER) with multi-grained word segmentation (MWS) and POS tagging tasks. |
| Outcome: | The proposed approach achieves better or comparable performance with current methods. |
Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context. |
| Approach: | They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework. |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine. |
A Pragmatic Approach for Classical Chinese Word Segmentation (L18-1)
Copied to clipboard
| Challenge: | Classical Chinese word segmentation is largely neglected due to its obsoleteness . a new approach to segmentation using a marked-up corpus is needed . |
| Approach: | They propose a pragmatic approach to deal with Classical Chinese word segmentation without any marked-up corpus. |
| Outcome: | The proposed method makes the CCWS without any marked-up corpus more accurate compared with collocation-based methods. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
Copied to clipboard
Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |