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Register a new userA Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging
نموذج مخصصات المجال الرخيصة من أجل تجزئة الكلمات المشتركة ووضع العلامات على نقاط البيع
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تعد مخصصات المجال لتقسيم الكلمات وعلامات نقاط البيع مشكلة صعبة للمعالجة المعجمية الصينية. التدريب الذاتي هو حل واعد فيه، الذي يكافح من أجل بناء مجموعة من مثيلات التدريب الزائفة عالية الجودة للنطاق المستهدف. عادة ما يفترض العمل السابق تكييفا عالميا من المصادر إلى الهدف لجمع مثل هذه الكائنات الزائفة، مما يتجاهل الفجوات المختلفة من الجمل المستهدفة إلى مجال المصدر. في هذا العمل، نبدأ من تجزئة الكلمات المشتركة ووضع علامات على نقاط البيع، وتقديم طريقة تكييف مجال Ground-Gromins لنموذج الفجوات بدقة. نقيس الفجوات بواسطة متري واحد بسيط وبديهي، واعتمادها لتطوير كوربوس المجال المستهدف الزائف بناء على النطاقات الفرعية المحبوبة بشكل جيد تدريجيا. يقترح نموذج التعلم التمثيل المختلط بين المجال الجديد وفقا لذلك لترميز المجال الفرعي المتعدد بشكل فعال. يتم تنفيذ العملية بأكملها تدريجيا لكل من Corpus Construction والنموذج التدريب. تظهر النتائج التجريبية على مجموعة بيانات معيار أن طريقتنا يمكن أن تكتسب تحسينات كبيرة على تختلف عن خطوط الأساس. يتم إجراء تحليلات واسعة لإظهار مزايا نموذج تكييف المجال النهائي لدينا أيضا.
Domain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing. Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain. Previous work usually assumes a universal source-to-target adaption to collect such pseudo corpus, ignoring the different gaps from the target sentences to the source domain. In this work, we start from joint word segmentation and POS tagging, presenting a fine-grained domain adaption method to model the gaps accurately. We measure the gaps by one simple and intuitive metric, and adopt it to develop a pseudo target domain corpus based on fine-grained subdomains incrementally. A novel domain-mixed representation learning model is proposed accordingly to encode the multiple subdomains effectively. The whole process is performed progressively for both corpus construction and model training. Experimental results on a benchmark dataset show that our method can gain significant improvements over a vary of baselines. Extensive analyses are performed to show the advantages of our final domain adaption model as well.
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