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Register a new userLexicon-Based Graph Convolutional Network for Chinese Word Segmentation
شبكة الرسوم الجاهزة المستندة إلى المعجم في المعجم
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يمكن أن تخفف المعلومات الدقيقة من حدود الكلمات مشكلة الغموض المعجمي لتحسين أداء مهام معالجة اللغة الطبيعية (NLP). وبالتالي، فإن تجزئة الكلمات الصينية (CWS) مهمة أساسية في NLP. نظرا لتطوير نماذج اللغة المدربة مسبقا (PLM)، فإن المعرفة المدربة مسبقا يمكن أن تساعد الأساليب العصبية في حل المشكلات الرئيسية ل CWS في إجراء كبير. حققت الطرق الحالية بالفعل أداء عال في العديد من المعايير (على سبيل المثال، bakeoff-2005). ومع ذلك، فإن الدراسات البارزة الحديثة محدودة من قبل كوربوس المشروح على نطاق صغير. لزيادة تحسين أداء أساليب CWS بناء على ضبط PLMS، نقترح إطار عمل عصبي رواية، LBGCN، الذي يشتمل على شبكة اتصالية قائمة بذاتها في الترميز في ترميز المحولات. النتائج التجريبية على خمسة معايير وأربعة مجموعات بيانات عبر المجال تظهر أن شبكة اتصال الرسوم البيانية المستندة إلى المعجم تستغرق بنجاح معلومات الكلمات المرشحة وتساعد على تحسين الأداء على المعايير (BakeOFF-2005 و CTB6) ومجموعات البيانات عبر المجال (Sighan- 2010). توضح المزيد من التجارب والتحليلات أن إطار عملنا المقترح نماذج المعجم بفعالية لتعزيز قدرة الأطر العصبية الأساسية وتعزز المتانة في سيناريو المجال العابر.
Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks. Thus, Chinese word segmentation (CWS) is a fundamental task in NLP. Due to the development of pre-trained language models (PLM), pre-trained knowledge can help neural methods solve the main problems of the CWS in significant measure. Existing methods have already achieved high performance on several benchmarks (e.g., Bakeoff-2005). However, recent outstanding studies are limited by the small-scale annotated corpus. To further improve the performance of CWS methods based on fine-tuning the PLMs, we propose a novel neural framework, LBGCN, which incorporates a lexicon-based graph convolutional network into the Transformer encoder. Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010). Further experiments and analyses demonstrate that our proposed framework effectively models the lexicon to enhance the ability of basic neural frameworks and strengthens the robustness in the cross-domain scenario.
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