14 research outputs found
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite
recent progress in neural-based CWS. The limited amount of annotated data in
the target domain has been the key obstacle to a satisfactory performance. In
this paper, we propose a semi-supervised word-based approach to improving
cross-domain CWS given a baseline segmenter. Particularly, our model only
deploys word embeddings trained on raw text in the target domain, discarding
complex hand-crafted features and domain-specific dictionaries. Innovative
subsampling and negative sampling methods are proposed to derive word
embeddings optimized for CWS. We conduct experiments on five datasets in
special domains, covering domains in novels, medicine, and patent. Results show
that our model can obviously improve cross-domain CWS, especially in the
segmentation of domain-specific noun entities. The word F-measure increases by
over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and
unsupervised cross-domain CWS approaches with a large margin. We make our code
and data available on Github
RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check
Chinese Spelling Check (CSC) refers to the detection and correction of
spelling errors in Chinese texts. In practical application scenarios, it is
important to make CSC models have the ability to correct errors across
different domains. In this paper, we propose a retrieval-augmented spelling
check framework called RSpell, which searches corresponding domain terms and
incorporates them into CSC models. Specifically, we employ pinyin fuzzy
matching to search for terms, which are combined with the input and fed into
the CSC model. Then, we introduce an adaptive process control mechanism to
dynamically adjust the impact of external knowledge on the model. Additionally,
we develop an iterative strategy for the RSpell framework to enhance reasoning
capabilities. We conducted experiments on CSC datasets in three domains: law,
medicine, and official document writing. The results demonstrate that RSpell
achieves state-of-the-art performance in both zero-shot and fine-tuning
scenarios, demonstrating the effectiveness of the retrieval-augmented CSC
framework. Our code is available at https://github.com/47777777/Rspell
Integrating Dictionary and Web N-grams for Chinese Spell Checking
Abstract Chinese spell checking is an important component of many NLP applications, including word processors, search engines, and automatic essay rating. Nevertheless, compared to spell checkers for alphabetical languages (e.g., English or French), Chinese spell checkers are more difficult to develop because there are no word boundaries in the Chinese writing system and errors may be caused by various Chinese input methods. In this paper, we propose a novel method for detecting and correcting Chinese typographical errors. Our approach involves word segmentation, detection rules, and phrase-based machine translation. The error detection module detects errors by segmenting words and checking word and phrase frequency based on compiled and Web corpora. The phonological or morphological typographical errors found then are corrected by running a decoder based on the statistical machine translation model (SMT). The results show that the proposed system achieves significantly better accuracy in error detection and more satisfactory performance in error correction than the state-of-the-art systems